# TOWARDS CONTINUAL KNOWLEDGE LEARNING OF LANGUAGE MODELS

**Joel Jang[1]** **Seonghyeon Ye[1]** **Sohee Yang[1]** **Joongbo Shin[2]**

**Janghoon Han[2]** **Gyeonghun Kim[2]** **Stanley Jungkyu Choi[2]** **Minjoon Seo[1]**

1KAIST AI 2LG AI Research
_{joeljang,vano1205,sohee.yang,minjoon}@kaist.ac.kr_
_{jb.shin,janghoon.han,ghkayne.kim,stanleyjk.choi}@lgresearch.ai_


ABSTRACT

Large Language Models (LMs) are known to encode world knowledge in their parameters as they pretrain on a vast amount of web corpus, which is often utilized
for performing knowledge-dependent downstream tasks such as question answering, fact-checking, and open dialogue. In real-world scenarios, the world knowledge stored in the LMs can quickly become outdated as the world changes, but
it is non-trivial to avoid catastrophic forgetting and reliably acquire new knowledge while preserving invariant knowledge. To push the community towards better maintenance of ever-changing LMs, we formulate a new continual learning
(CL) problem called Continual Knowledge Learning (CKL). We construct a new
benchmark and metric to quantify the retention of time-invariant world knowledge, the update of outdated knowledge, and the acquisition of new knowledge.
We adopt applicable recent methods from literature to create several strong baselines. Through extensive experiments, we find that CKL exhibits unique challenges that are not addressed in previous CL setups, where parameter expansion is
necessary to reliably retain and learn knowledge simultaneously. By highlighting
the critical causes of knowledge forgetting, we show that CKL is a challenging and
important problem that helps us better understand and train ever-changing LMs.
The benchmark datasets, model checkpoints, and code to reproduce our results are
[available at this https URL.](https://github.com/joeljang/continual-knowledge-learning)

1 INTRODUCTION

Recent works have shown that large Language Models (LM), such as T5 (Raffel et al., 2019) and
GPT-3 (Brown et al., 2020), have the capability of storing a tremendous amount of world knowledge
in their parameters when pretrained on a vast corpus of text (Petroni et al., 2019). These pretrained
LMs have shown potential to serve as knowledge bases when probed for world knowledge without
any finetuning through the LAnguage Model Analysis (LAMA) task (Petroni et al., 2019), which
requires probing LMs for world knowledge in a zero-shot manner through slot-filling, and promising
results utilizing the encoded world knowledge when finetuned on various Knowledge Intensive Language Tasks (KILT) (Petroni et al., 2021), e.g., question answering, knowledgeable open dialogues.

While the world knowledge stored in LMs has diverse use cases, it can quickly become outdated
as the world changes fast, and LMs need to frequently renew their internal world knowledge accordingly. For example, it is impossible to probe for new information such as “ _won the US_
_Election 2020” from the original T5 (Raffel et al., 2019) which was pretrained on C4 web corpus_
from April 2019.[1] Also, information that may have once been considered accurate may no longer
be valid because the information has been updated. For instance, the answer to “Which soccer team
_does Cristiano Ronaldo play for?” has changed from Juventus to Manchester United in September_
2021. Meanwhile, time-invariant information learned from the original corpus such as “Barack
_Obama was born in Honolulu, Hawaii” should not be altered within the LMs._

1T5 was initially pretrained on the C4 dataset (about 750 GB), which is a cleansed dump of Common Crawl
extracted from the web in April 2019.


-----

**Random LMs** !! **Pretrained LMs** !" **Continually Pretrained LMs**

**T5** **GPT-2** **T5** **GPT-2** **T5** **GPT-2**

**_LM Pretraining_**

**_LM Pretraining_** **_(continued)_**

**INVARIANTLAMA** **UPDATEDLAMA** **NEWLAMA** **CKL Benchmark**


Figure 1: Overview of the CONTINUAL KNOWLEDGE LEARNING benchmark. INVARIANTLAMA is used to
measure the time-invariant world knowledge gained from D0. UPDATEDLAMA is used to measure the update
of world knowledge from D0 →D1. NEWLAMA is used to measure new world knowledge gained from D1.

Despite its importance, the challenge of renewing the internal world knowledge stored in the parameters of LMs is nontrivial and has only been explored in rather specific settings. For example,
recent works have proposed to modify specific target knowledge such as individual facts (De Cao
et al., 2021; Zhu et al., 2020; Dai et al., 2021). Dhingra et al. (2021) have addressed LMs as temporal knowledge bases by jointly modeling text with its timestamp. But the problem of renewing the
world knowledge of LMs in a more general and scalable way, such as through continual pretraining
on a corpus with new knowledge, has not been formally formulated or explored by previous works.
Moreover, the community lacks a benchmark that can be used to systematically study how the internal knowledge of LMs changes through the training on new information. Lastly, methodologies to
effectively renew the knowledge of LMs at scale have yet to be thoroughly explored.

In this work, we propose a novel continual learning (CL) formulation named CONTINUAL KNOWLEDGE LEARNING (CKL), where we attempt to renew the internal world knowledge of LMs through
continual pretraining on new corpora. We systematically categorize world knowledge into three
main categories and make benchmark datasets to measure each of them during CKL: (1) INVARIANTLAMA for time-invariant world knowledge in LMs that should not be forgotten or altered,
(2) UPDATEDLAMA for outdated world knowledge that needs to be updated in the LMs, and (3)
NEWLAMA for new world knowledge that should be injected into the LMs. We also propose a
novel metric named FUAR (FORGOTTEN / (UPDATED + ACQUIRED) RATIO) that can measure
the trade-off between forgetting, updating, and acquiring knowledge. Finally, while one might think
of implementing contemporary CL methods for this benchmark, we show that CKL has nontrivial differences to traditional CL formulations and require approaches specific to CKL. We find and
compare model architectures and training methodologies (Chen et al., 2020; He et al., 2021; Hu
et al., 2021; Wang et al., 2021b) from the literature that have shown potential to mitigate forgetting
of knowledge gained during pretraining, establishing them as baselines for the CKL benchmark.

In sum, while the challenge of renewing the internal world knowledge of LMs is essential in realworld scenarios, it has yet to be formulated or extensively explored. Therefore, in this paper:

-  We propose a novel CL formulation called CONTINUAL KNOWLEDGE LEARNING (CKL)
and construct a new benchmark to measure the amount of forgetting and amount of world
knowledge gained by continued pretraining on a novel language modeling corpus that we
construct, containing new knowledge.

-  We explore LM architectures and training methodologies that are natural baselines for CKL
in literature, denoting them as CKL methods, and performing extensive experiments on
our CKL benchmark. We categorize them into regularization, rehearsal, and parameterexpansion methods, same as in traditional CL literature, and compare the effectiveness of
each type of method using a novel metric named FUAR that we propose to measure the
trade-off between forgotten knowledge and updated or acquired knowledge.

-  Towards creating an ever-changing LM, we perform extensive analysis in the CKL benchmark and highlight important challenges and findings: parameter-expansion methods have
the limitation of memory inefficiency despite performing the best in most of our experiments and seeing the same data repeatedly during continued pretraining is a critical cause
of forgetting. Also, we show interesting results that need further exploration: learning rate
can be varied to balance the forgetting and learning of new knowledge, CKL may help in


-----

performing previous-knowledge-intensive tasks after gaining new world knowledge, and
CKL methods are transferable across LM architectures despite showing a different trend in
performance.

An overview of the proposed CKL benchmark is shown in Figure 1.

2 RELATED WORK

Language Models (LMs) utilizing knowledge from external sources, such as Retrieval-Augmented
Generation (RAG) (Lewis et al., 2020a) and Blender Bot 2.0 (Xu et al., 2021; Komeili et al., 2021),
cope with the changing world by updating the external sources during inference or searching the
internet for retrieving recent information. However, recent works have shown that these memoryaugmented models suffer from hallucination, which means that they present false information as
if it were correct, despite being given updated knowledge during inference (Zhang & Choi, 2021),
which worsens as the size of the LM increases (Longpre et al., 2021), making it more so important
for implicit parameters to be renewed as well.

In order to renew the internal knowledge of LMs, one might consider pretraining LMs from scratch
with a newly updated text corpus of a scale similar to the one used during initial pretraining, such
as a recent dump of the entire Wikipedia. However, this approach is computationally demanding
and also environmentally harmful (Patterson et al., 2021). Another alternative approach is continuing the pretraining process on a much smaller corpus containing new world knowledge, but such
a methodology is known to suffer from catastrophic forgetting (McCloskey & Cohen, 1989; Kirkpatrick et al., 2017), where the models forget previously learned knowledge as they acquire new
knowledge.

Lazaridou et al. (2021); Jin et al. (2021) suggests implementing prior Continual Learning (CL)
methods (Sun et al., 2020; d’Autume et al., 2019) to address this problem. However, it is important to note that there are nontrivial differences between traditional CL and the proposed Continual
Knowledge Learning (CKL) formulation which make applying traditional CL methods inadequate.
In traditional CL, methods can be largely categorized into regularization, rehearsal, and parameter_expansion methods. (1) While regularization methods (Kirkpatrick et al., 2017) require identifying_
important parameters used for previous tasks, exactly how and where the knowledge is stored in
the parameters of an LM is currently extremely difficult to identify and localize (Vig et al., 2020;
De Cao et al., 2021). (2) While prior rehearsal methods (Lopez-Paz & Ranzato, 2017) consider
learning all of the streams of tasks at once (multi-task learning) as the performance upper-bound
and replicate such a setting with samples stored in the episodic memory, a few samples from the
pretraining corpus cannot represent the overall world knowledge from the corpus. Moreover, if LMs
are pretrained on a shuffled concatenation of stream of corpora, there is no guarantee that the LMs
will acquire the correct, recent information from the recent corpora, especially in cases where the
former corpora are much bigger than the latter ones, which is shown by experiments in Section 5.1.
(3) Lastly, prior parameter-expansion methods (Rusu et al., 2016; Yoon et al., 2018) focus on learn_ing a stream of different tasks via strong supervision, while in CKL, the focus is constantly updating_
_world knowledge from a stream of corpora via self-supervision._

Because of these fundamental differences, instead of contemporary CL methods mentioned above,
we explore methodologies from the literature that are suitable for CKL (Chen et al., 2020; He et al.,
2021; Hu et al., 2021; Wang et al., 2021b), modifying and adapting each method according to our
needs as CKL methods. Lastly, while it has been pointed out that some of the traditional CL formulations may have little practical importance in real-world scenarios by Prabhu et al. (2020), CKL is
much closer to the initial motivation behind CL, which is that the “fundamental characteristic of natural intelligence is its ability to continually learn new knowledge while updating information about
the old ones” (Prabhu et al., 2020). Details of related works regarding the traditional CL methods
and how CKL methods address the fundamental differences are provided in Appendix A.

3 CONTINUAL KNOWLEDGE LEARNING (CKL)

In this section, we explain the formulation of the task, the data construction process, and the proposed metric measuring the trade-off between forgetting previous world knowledge and updating
and learning of new world knowledge.


-----

3.1 TASK FORMULATION

When viewing the task of renewing the internal knowledge of LMs as one of CL formulations,
pretraining on the original corpus can be considered as a previous task, and continued pretraining
on new corpus can be considered as the current task, the main objective becoming retaining the
_time-invariant world knowledge gained through initial pretraining while efficiently learning new_
and updated world knowledge through continued pretraining. Throughout the paper, we let D0
refer to the corpus used for initial pretraining and let D1 denote the new corpus used for continued
pretraining.

**New Text Corpus for Language Modeling** For LMs to renew their internal knowledge, they need
to be continually pretrained on a new text corpus D1 which has the updated and new information.
_D1 should ideally be much smaller than D0, as a large D1 amounting to the size of D0 will result in_
massive computational costs similar to pretraining the LMs from scratch. For constructing D1, we
crawl recently published news articles from the web making CC-RECENTNEWS.[2]

**Probing LMs for World Knowledge** The most widely used task for probing LMs for world
knowledge is the LAnguage Model Analysis (LAMA) (Petroni et al., 2019) task, which consists
of cloze sentences created from a set of knowledge sources using manually defined templates. We
define that an LM knows a fact if it can successfully predict in a zero-shot manner the masked entity
in the cloze sentence, such as “Dante was born in ” as Florence. While there may be other
alternatives for measuring the world knowledge encoded in LMs[3], we construct our main datasets
as LAMA tasks, while also additionally providing the corresponding question pairs to the cloze
sentences for those who want to test on CBQA as well.

**Measuring Retention of Time-invariant World Knowledge** We define time-invariant world
knowledge as the information present in D0 that has no possibility of conflicting with information from D1. For example, if the information of the birthplace of Barack Obama is present in D0, it
is unlikely that D1 contains information that contradicts that fact. Also, we classify instances where
the time-stamps are fixed such as “Cristiano Ronaldo played for _in 2010.” as time-invariant._
These time-invariant instances should not be changed as LMs are continually pretrained on D1. In
order to measure how much time-invariant information is lost due to catastrophic forgetting during continued pretraining, we create INVARIANTLAMA, a subset of LAMA (Petroni et al., 2019),
consisting of only time-invariant cloze sentences detailed in Appendix B.1.

**Measuring Update of Outdated World Knowledge** In this work, we define outdated world
knowledge as information that is conflicting between D0 and D1. For example, the President of
the US may be Barack Obama in D0 and Joe Biden in D1. In this case, the LM should update its
internal knowledge as Joe Biden as the US president. If an LM is pretrained on both D0 and D1
simultaneously, there is no guarantee that the LM will acquire the correct, recent information from
_D1, especially in cases where D0 is much bigger than D1, which is one of the biggest difference_
between the CKL and traditional CL setting. For measuring update of outdated information, we
construct UPDATEDLAMA which is made up of cloze statements for which answers can be found
in both D0 and D1, but are conflicting.

**Measuring Acquisition of New World Knowledge** We define new world knowledge as the information present in D1, but not in D0. To measure new knowledge acquired through continued
pretraining on D1, we construct NEWLAMA which is made up of detailed cloze statements requiring new knowledge from D1 to correctly answer. We provide two datasets for measuring new world
_knowledge: NEWLAMA, for which each of the instances is verified that the answer does not exist_
in D0, but only in D1, and NEWLAMA-EASY for which each of the instances does not perfectly
comply with our strict definition of new world knowledge due to its creation process, but is used to
generally measure the new knowledge acquired from continued pretraining on D1 at a larger scale.

2
CC-RECENTNEWS consists of 221,779 articles (∼168M tokens), which is estimated to be about 750 times
[smaller than C4, a cleansed version of the April 2019 Common Crawl dataset (https://commoncrawl.org/) that](https://commoncrawl.org/)
was used to initially pretrain the T5 LM (Raffel et al., 2019).
3Closed-book question answering (CBQA) (Roberts et al., 2020) can also be considered as a task that measures the world knowledge of LMs through finetuning, but it has been pointed out that much of its performance
increases are due to the test-train overlap (Lewis et al., 2020b; Wang et al., 2021a) in the datasets.


-----

Table 1: Dataset statistics. Input and answer length are the corresponding average token lengths.

|Dataset Size Input Length Answer Length|Dataset Size Input Length Answer Length|
|---|---|


|INVARIANTLAMA 17474 11.9 1.3 UPDATEDLAMA 924 13.7 9.4|NEWLAMA 797 14.7 8.7 NEWLAMA-EASY 11177 44.4 6.1|
|---|---|



NEWLAMA-EASY can be considered easier since each instance was constructed to be similar to
the data distribution seen during continued pretraining.

**Dataset Construction** The data for continual pretraining, CC-RECENTNEWS, is constructed using news-please (Hamborg et al., 2017). INVARIANTLAMA is constructed by manually selecting 28
_time-invariant relations from T-Rex (Elsahar et al., 2018). For UPDATEDLAMA and NEWLAMA,_
we use Amazon Mechanical Turk (mturk)[4] for crowd-sourcing Human Intelligent Tasks (HITs).
The process requires selecting answerable questions from a list of questions generated by the model
introduced in Lewis et al. (2021) and converting them into cloze sentences. We have also separately
hired 11 experts to verify the correctness and search the C4 database to categorize each instance
following our definition of updated and new. NEWLAMA-EASY is constructed at a larger scale
through a two-phase mturk process where sentences selected from articles containing new information are decontextualized and paraphrased[5] before being masked, verified and converted to corresponding questions. The constructed dataset statistics are in Table 1. Important details about the
data construction pipeline, examples, and more fine-grained statistics are provided in Appendix B.

3.2 COMBINED METRIC FOR CKL

We propose a novel metric, FUAR (FORGOTTEN / (UPDATED + ACQUIRED) RATIO), that can
compare the efficiency of each CKL method using the trade-off between forgotten time-invariant
knowledge and updated or newly acquired knowledge. FUAR represents relatively how many timeinvariant knowledge instances are forgotten in order to learn one new or updated knowledge instance.
We first define FUAR for the general case where there can be multiple corpora used for training an
ever-changing LM.

Let T be an arbitrary task and (Di)[n]i=0 [be a sequence of corpora used for LM pretraining, where][ D][0]
is the initial pretraining corpus. We define Gap(T, _Da,_ _Db) = Score(T_ ) of LMa − _Score(T_ ) of LMb,
where LMa represents the LM after being pretrained on Da. Then, we denote T[F] = (Ti[F] [)]i[n]=[−]0[1] [as a]
sequence of tasks from (Di)[n]i=[−]0[1] [measuring the forgetting of invariant-knowledge from each corre-]
sponding corpous. If there is no such task from corpus Di, the value of Ti[F] is set to n.d., which
means not defined. Likewise, we denote Tn[U] [and][ T]n[ A] [as tasks from][ D][n] [measuring the][ update][ and]
_acquisition of new knowledge, respectively. We define FUAR as follows:_

_n−1_

 _i=∑0_ max(0, Gap(Ti[F] _[,]_ _[D][i][,]_ _[D][n][))][1]{Ti[F]_ _[̸][=][n][.][d][.][}]_ _,_

FUAR(T[F] _,_ _Tn[U]_ _[,]_ _[T]n[ A][) =]_  _ni=∑−01{max(0,_ Gap(Tn[U] _[,]_ _[D][n][,]_ _[D][i][))][1]{Ti[F]_ _[̸][=][n][.][d][.][}][ +]_ [max][(][0][,] [Gap][(][T]n[ A][,] _[D][n][,]_ _[D][i][))][1]{Ti[F]_ _[̸][=][n][.][d][.][}][}]_

if denominator > 0,
The choice of benchmark tasksno gain, otherwise. T[F], Tn[U] [, and][ T]n[ A] [can differ according to each experimental setup.](1)
FUAR value of 1.0 represents an equal trade-off scenario where one time-invariant knowledge instance of T[F] is forgotten on average to gain one new or updated knowledge instance of Tn[U] and
_Tn[A][. The two terms in the denominators are summed because newly gained knowledge and updated]_
knowledge are mutually exclusive by definition. When the value is smaller than 1, it means that the
model obtains more new or updated knowledge than the amount of forgotten knowledge, so methods

[4https://www.mturk.com](https://www.mturk.com)
5Decontextualization model from Choi et al. (2021) and back-translation model from Tiedemann & Thottingal (2020) is used.


-----

that exhibit a low FUAR value can be considered suitable for CKL. If the value is zero, then it is a
case where no forgetting occurs at all and is the upper bound for performance. If the denominator is
0, we denote the case as no gain and regard it as the worst possible case.[6]

4 EXPERIMENTAL SETUP

We perform extensive experiments with an encoder-decoder model, T5 (Raffel et al., 2019), a
large LM ([∼] 737M params) initially pretrained on April 2019 dump of C4 and May 2020 dump
of Wikipedia (thus D0 in our experiments) with salient span masking (SSM). The details of the pretraining, continual pretraining, and evaluation configurations are in Appendix C. We establish the
following methods as the baselines for the CKL benchmark and categorize them into regularization,
_rehearsal, and parameter-expansion methods. The specific hyperparamters used for the implemen-_
tation of each method are detailed in Appendix D.

**Initial refers to the setting where we evaluate the LM before any continued pretraining. The perfor-**
mance of this model can be considered as the upper-bound for INVARIANTLAMA and lower-bound
on UPDATEDLAMA and NEWLAMA.

**Vanilla is a specific setting of further pretraining (Gururangan et al., 2020), where the domain is**
_new knowledge, and the LM is further pretrained without any training strategies._

**RecAdam (Chen et al., 2020) falls into the category of regularization methods. It places a stronger**
independent assumption among the model parameters than the traditional regularization method
(EWC (Kirkpatrick et al., 2017)) and does not access the initial pretraining corpus to regularize the
model weights during continued pretraining. The optimizer is annealed so that less regularization is
applied as the training progresses.

**Mix-Review (He et al., 2021) falls into the category of rehearsal methods, which assumes access to**
the initial pretraining corpus and mixes in random subsets of the initial pretraining data during continued pretraining, depending on the mix-ratio at the current time step. As the training progresses,
the mix-ratio decays towards 0, decreasing the amount of the mixed original data at each iteration.

**LoRA (Hu et al., 2021) falls into the category of parameter-expansion methods. It freezes the**
original parameters of the LM and adds trainable rank-decomposition matrices into each layer that
are updated during continued pretraining. Hu et al. (2021) has implemented this approach with
decoder-only models (GPT-2 (Radford et al., 2019) & GPT-3 (Brown et al., 2020)) while we apply
it to an encoder-decoder model, denoting it as T5-LoRA.

**K-Adapter (Wang et al., 2021b) is another parameter-expansion method that freezes the original**
parameters of the LM while adding k number of new layers, namely adapters, that are updated
during continued pretraining. Wang et al. (2021b) have shown successful injection of factual and
_linguistic knowledge for encoder-only models, BERT (Devlin et al., 2019) & RoBERTa (Liu et al.,_
2019), while we also apply it to an encoder-decoder model, T5, and decoder-only model, GPT-2.

**Modular is a newly proposed parameter-expansion method specifically for encoder-decoder models**
which freezes the original, pretrained encoder while adding a new, randomly initialized encoder that
is updated during continued pretraining. For the newly added encoder, we vary the size to T5-small
while keeping the size of the original encoder and decoder to be T5-large.

5 EXPERIMENTAL RESULTS

In this section, we first show the main experimental results for the CKL Benchmark. Then, since
multiple steps of continual knowledge learning, i.e., CKL are needed for training a true, everchanging LM, we explore the effects of multiple CKL phases as well as how epochs, corpus size,
and the total number of training steps affect CKL. We further explore how learning rates affect CKL
in Appendix E, how continual pretraining on D1 affects the performance of KILT tasks which re
6Each of the last two sentences means that we do not measure positive backward transfer and negative for_ward transfer, respectively. The latter in some cases actually do happen (shown in Appendix G). Explanations_
about the backward and forward transfer are in Appendix A.1.


-----

Table 2: Zero-shot probing performance on the CKL benchmark. The best results for each task and metric are
shown in bold, and the second-best results are underlined.


**IL** **UL** **NL** **NLE**
**FUAR**
EM EM EM EM ((IL), **UL,** **NL) ↓**


**# of Params**
**Method**
**(Trainable / Total)**


T5-Initial 0M / 737M **24.17** 1.62 1.88 10.32 - 

T5-Vanilla 737M / 737M 12.89 10.17 3.77 17.75 1.08
T5-RecAdam 737M / 737M 13.20 12.55 4.02 17.85 0.84
T5-MixReview 737M / 737M 13.92 6.49 2.89 14.86 1.74
T5-LoRA 403M / 738M 16.58 12.77 4.52 19.56 0.55
T5-Kadapters (k=2) 427M / 762M 19.59 12.34 5.03 18.75 0.33

T5-Kadapters (k=3) 440M / 775M 19.76 12.66 4.02 19.00 0.33

T5-Modular 438M / 773M 20.29 12.66 4.65 19.24 **0.28**

quire knowledge from D0 in Appendix F, how CKL methods transfer across LM architectures in
Appendix G, and how the prediction outputs change during CKL in Appendix H.

5.1 MAIN RESULTS

Table 2 shows our main experimental result on the CKL benchmark. While only the exact match
(EM) is reported in Table 2, we report the F1 score as well as the mean precision at k (P@k,
k=1,5,10,20,50,100) in Appendix J. The T5 models are originally pretrained on C4 (about 1 trillion token updates) and Wikipedia, which is considered as D0.[7], and then continually pretrained
on CC-RecentNews (corpus D1) for 4 epochs (25k global training steps, about 673 million token
updates) using each of the CKL methods. Each of IL, UL, NL, NLE stands for INVARIANTLAMA,
UPDATEDLAMA, NEWLAMA, and NEWLAMA-EASY, respectively. Detailed descriptions about
the setup for this experiment are included in the caption.

We first find that all of the CKL methods except for T5-MixReview are more effective at forgetting
less time-invariant knowledge while updating and acquiring new knowledge than using the na¨ıve approach of T5-Vanilla as shown by the FUAR. This result also highlights the main difference between
CKL and CL; while rehearsal methods show strong performances in traditional CL settings (Prabhu
et al., 2020; Bang et al., 2021), in CKL, it shows the worst performance since the update of outdated
knowledge and acquisition of new knowledge is severely deterred as shown in the performance of
UL and NL while not showing competitive mitigation of forgetting as shown in the performance of
IL compared to other CKL methods. Amongst the other CKL methods, we observe a rather consistent trend that the parameter-expansion methods achieve better results. The first and second-best
results on all of UL, NL, and NLE are all from parameter-expansion methods. Meanwhile, although
UL and NL are constructed following the same procedure, there is a huge difference between the
EM scores of UL and NL. We analyze the source of this difference in Appendix I.

Figure 9 visualizes how the EM scores of each task change as T5-Kadapters, the CKL method with
the most robust performance, and T5-Vanilla are continually pretrained on D1. In all of the tasks,
the performance of T5-Initial can be considered as the upper-bound for IL and lower-bound for UL,
NL, NLE. Corresponding with our main observations, CKL allows considerable retention of time_invariant world knowledge while improving updating and gaining new world knowledge compared_
to T5-Vanilla, mitigating the overall trade-off.

5.2 EXPLORING MULTIPLE PHASES OF CKL

In order to show the potential for creating a truly ever-changing LM, we explore the effect of multiple
CKL phases by creating CC-RECENTNEWS-SMALL, denoted as SMALL, which is a small variant
of CC-RECENTNEWS that consists of randomly sampled 10% of the original corpus. We then split

7In this work, we see C4 and Wikipedia together as D0, because we do not measure how the knowledge in
LMs change in between training on those two corpora.


-----

25

20

15

10

|T5-Initial T5-Vanilla T5-Kadapters (k=2)|Col2|Col3|Col4|Col5|
|---|---|---|---|---|
|24 12 5.5 19 20 8 4.5 16 EM EM EM EM 3.5 16 4 13 2.5 12 0 1.5 10 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4|||||
||||||
||||||
||||||


Epochs

(a) INVARIANTLAMA


Epochs

(b) UPDATEDLAMA


Epochs

(c) NEWLAMA


Epochs

(d) NEWLAMA-EASY


Figure 2: Performance at each epoch during continued pretraining in the main experimental setting.Title

Table 3: Zero-shot probing performance after T5 models are continually pretrained on different subsets of
CC-RECENTNEWS. NLE and IL stand for NewLAMA-Easy and InvariantLAMA, respectively. There are
three scenarios according to the corpus used for continual pretraining, explained in the text of Section 5.2. The
FUAR of the three scenarios is calculated differently, and the corresponding tasks are shown in the table as the
parameters of FUAR: T[F], Tn[U] [, and][ T]n[ A][. In this setting,][ T][F][ consists of only a single task][ T]0[ F] [(IL) measuring the]
time-invariant information lost from D0 only. For SMALL, we calculate the gap on NLE using the weighted
sum of the gaps on NLEP1 and NLEP2 with uniform weights.


**IL** **NLEP1** **NLEP2**

EM EM EM


**# of Params**
**Corpus** **Method**
**(Trainable / Total)**


**FUAR**
T5-Initial 0M / 737M **24.17** 8.69 9.45 ((IL), **_n.d.,_** **NLE) ↓**

T5-Vanilla 737M / 737M 11.86 17.77 16.42 1.53
T5-RecAdam 737M / 737M 11.85 16.46 13.93 2.01
T5-MixReview 737M / 737M 14.36 14.18 13.93 1.97
T5-LoRA 403M / 738M 14.26 20.60 19.90 0.87
T5-Kadapters (k=2) 427M / 762M 18.16 18.34 16.42 0.72

T5-Kadapters (k=3) 440M / 775M 17.12 **20.98** **20.39** **0.61**
T5-Modular 438M / 773M 16.40 19.47 19.90 0.73

**FUAR**
T5-Initial 0M / 737M **24.17** 8.69 9.45 ((IL), **_n.d.,_** **NLEP1) ↓**

T5-Vanilla 737M / 737M 9.68 20.60 _11.44_ 1.22
T5-RecAdam 737M / 737M 11.78 20.42 _11.94_ 1.06
T5-MixReview 737M / 737 M 16.13 15.88 _11.94_ 1.12
T5-LoRA 403M / 738M 14.75 **20.79** _13.93_ 0.78
T5-Kadapters (k=2) 427M / 762M 19.11 20.60 _10.95_ **0.42**
T5-Kadapters (k=3) 440M / 775M 19.08 18.15 _10.94_ 0.54

T5-Modular 438M / 773M 17.08 18.90 _11.94_ 0.69

**FUAR**
T5-Initial 0M / 737M **24.17** 8.69 9.45 ((IL, **_n.d.),_** **_n.d.,_** **NLEP2) ↓**

T5-Vanilla 737 M / 737 M 9.40 14.37 **23.38** 1.06
T5-RecAdam 737M / 737M 7.25 14.56 20.90 1.48
T5-MixReview 737M / 737M 13.20 **17.20** 16.92 1.47
T5-LoRA 404M / 740M 13.25 16.07 22.39 0.84
T5-Kadapters (k=2) 427M / 788M 15.78 16.07 **23.38** **0.60**
T5-Kadapters (k=3) 440M / 813M 15.47 15.31 20.90 0.76

T5-Modular 438M / 809M 14.66 15.31 20.40 0.87


SMALL
(SMALL-P1
+ SMALL-P2)

SMALL-P1

SMALL-P1→
SMALL-P2


CC-RECENTNEWS-SMALL into two different splits by the published date of each article to simulate
a setting where multiple CKL phases are needed, denoted as SMALL-P1 (05.2020 - 11.2020)) and
SMALL-P2 (11.2020 - 04.2021). NLE[8] is also split into two different, smaller datasets, NLEP1
and NLEP2, each comprising of instances constructed from articles in SMALL-P1 and SMALL-P2,

8
We use NEWLAMA-EASY instead of NEWLAMA because the number of instances in NL corresponding
to articles from SMALL is too small for robust evaluation.


-----

25

20

15

10


25

20

15

10


|Col1|Col2|Col3|Col4|Col5|Col6|
|---|---|---|---|---|---|
|||||||
|||T||5-Initial||
|Sm|all-P1||M|ain||
||||S S|mall mall-P1→|Small-P2|
|2500 5000 10000 15000 20000 25||||||


Global Training Steps


|Col1|Col2|Col3|Col4|Col5|Col6|
|---|---|---|---|---|---|
|||||||
|Sm|all-P1||T|5-Initial||
||||M S|ain mall||
||||S|mall-P1→|Small-P2|
|0 2500 5000 10000 15000 20000 25||||||


Global Training Steps


(a) T5-Vanilla


(b) T5-Kadapters (k=2)


Figure 3: Performance at each epoch on INVARIANTLAMA during continued pretraining in MAIN, SMALL,
and SMALL-P1→SMALL-P2 scenarios. Each marker indicates the result at each continual pretraining epoch.

respectively. We compare how CKL methods for T5 perform on IL, NLEP1, and NLEP2 when
continually pretrained entirely on SMALL for 5k steps (8 epochs), and when sequentially pretrained
on SMALL-P1 and then on SMALL-P2 for 2.5k steps (8 epochs) each. In the scenario SMALLP1→SMALL-P2, there are two CKL phases where D0 is C4 and Wikipedia, D1 is SMALL-P1, and
_D2 is SMALL-P2. The rest of the configurations are set identical with the main experiments._

Comparing the performance on IL of the two scenarios, SMALL and SMALL-P1→SMALL-P2, results show that LMs are prone to more forgetting as they go through multiple CKL phases, despite
having the same number of training steps. One of the reasons may be due to the learning rate
scheduling, which is initialized at the start of each phase.

Furthermore, despite showing the best performance overall, the drawbacks of parameter-expansion
methods are also highlighted in the SMALL-P1→SMALL-P2 setting; they require new parameters to
be added at every phase of the update. For example, the number of total parameters of T5-Modular
increases by 36M in every round of the continual pretraining phase. Likewise, considering a large
number of CKL phases introduces new problems that should be additionally studied. Taking into
account that LMs should be updated frequently with a small amount of data in real-world scenarios
for gaining up-to-date world knowledge about the ever-changing world in a computation-effective
manner, more research is needed to mitigate the amount of forgetting that follows the larger number
of update phases.

**Effects of Epochs, Corpus Size, and Total Number of Training Steps in CKL on Forgetting**
Figure 3 shows the result of T5-Vanilla and T5-Kadapters during continued pretraining in different
scenarios from Table 2 and 3, where each point in the graph represents the performance of IL after
every epoch. Comparing MAIN (4 epochs) and SMALL (8 epochs) in Figure 3 (a) T5-Vanilla, we can
see that more forgetting occurs in SMALL, even though trained for five times less number of global
training steps. This phenomenon is further highlighted when comparing results from SMALL-P1 (8
epochs) which shows the most amount of forgetting despite being trained for ten times less number
of global training steps. While the overall drop is much mitigated in Figure 3 (b) T5-Kadapters, we
observe the same trend between each scenario which goes to show how critical observing the same
data repeatedly during continued pretraining is for causing forgetting.

The results are in line with findings from Lee et al. (2021) which suggest LMs should be pretrained
with just a few epochs on less duplicating data for efficiency. We add additional intuition to their
findings and conjecture that the inefficiency of pretraining from duplicate data could have been
caused by the forgetting of the rather long-tail knowledge in the pretraining corpus.


6 CONCLUSION

In this paper, we propose CONTINUAL KNOWLEDGE LEARNING (CKL), where we establish
benchmark datasets and metrics, and explore methodologies towards continual knowledge learning
of an ever-changing LM. We find that parameter-expansion methods show the most robust performance throughout all of the experimental settings, which nevertheless has severe memory inefficiency and that seeing the same data often is a critical cause of forgetting. We also discuss several
other interesting results of which we leave further exploration to future studies. To this end, we
suggest the community to explore CKL for the better design of an ever-changing LM.


-----

ACKNOWLEDGMENTS

The authors would like to thank Sang-Woo Lee, Jinheon Baek, Miyoung Ko, Hyunji Lee, and Eunbi
Choi for helpful discussions. This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.
2019-0-00075, Artificial Intelligence Graduate School Program (KAIST)).

REFERENCES

Jihwan Bang, Heesu Kim, YoungJoon Yoo, Jung-Woo Ha, and Jonghyun Choi. Rainbow memory:
Continual learning with a memory of diverse samples. In CVPR, 2021.

Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal,
Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are
few-shot learners. In NeurIPS, 2020.

Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, and Xiangzhan Yu. Recall and
learn: Fine-tuning deep pretrained language models with less forgetting. In EMNLP, 2020.

Eunsol Choi, Jennimaria Palomaki, Matthew Lamm, Tom Kwiatkowski, Dipanjan Das, and Michael
Collins. Decontextualization: Making sentences stand-alone. TACL, 9:447–461, 2021.

Damai Dai, Li Dong, Y. Hao, Zhifang Sui, and Furu Wei. Knowledge neurons in pretrained transformers. ArXiv, abs/2104.08696, 2021.

Cyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, and Dani Yogatama. Episodic
memory in lifelong language learning. In NeurIPS, 2019.

Nicola De Cao, Wilker Aziz, and Ivan Titov. Editing factual knowledge in language models. In
_EMNLP, 2021._

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep
bidirectional transformers for language understanding. In NAACL, 2019.

Bhuwan Dhingra, Jeremy R Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, and
William W Cohen. Time-aware language models as temporal knowledge bases. arXiv preprint
_arXiv:2106.15110, 2021._

Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. Wizard
of wikipedia: Knowledge-powered conversational agents. In ICLR, 2019.

Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Elena Simperl, and Frederique Laforest. T-rex: A large scale alignment of natural language with knowledge
base triples. In LREC, 2018.

Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. Eli5:
Long form question answering. In ACL, 2019.

Zhaochen Guo and Denilson Barbosa. Robust named entity disambiguation with random walks.
_Semantic Web, 9(4):459–479, 2018._

Suchin Gururangan, Ana Marasovi´c, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey,
and Noah A Smith. Don’t stop pretraining: adapt language models to domains and tasks. In ACL,
2020.

Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. Realm: Retrievalaugmented language model pre-training. In ICML, 2020.

Felix Hamborg, Norman Meuschke, Corinna Breitinger, and Bela Gipp. news-please: A generic
news crawler and extractor. In 15th International Symposium of Information Science (ISI 2017),
pp. 218–223, 2017.


-----

Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James Glass, and Fuchun Peng. Analyzing the forgetting problem in pretrain-finetuning of open-domain dialogue response models.
In EACL, 2021.

Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen F¨urstenau, Manfred Pinkal, Marc
Spaniol, Bilyana Taneva, Stefan Thater, and Gerhard Weikum. Robust disambiguation of named
entities in text. In EMNLP, 2011.

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu
Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685,
2021.

Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew Arnold, and
Xiang Ren. Lifelong pretraining: Continually adapting language models to emerging corpora.
_arXiv preprint arXiv:2110.08534, 2021._

Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly
supervised challenge dataset for reading comprehension. In ACL, 2017.

James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A
Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences,
114(13):3521–3526, 2017.

Mojtaba Komeili, Kurt Shuster, and Jason Weston. Internet-augmented dialogue generation. arXiv
_preprint arXiv:2107.07566, 2021._

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris
Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a
benchmark for question answering research. TACL, 7:453–466, 2019.

Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Sebastian Ruder, Dani Yogatama, et al.
Pitfalls of static language modelling. arXiv preprint arXiv:2102.01951, 2021.

Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris CallisonBurch, and Nicholas Carlini. Deduplicating training data makes language models better. arXiv
_preprint arXiv:2107.06499, 2021._

Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. Zero-shot relation extraction via
reading comprehension. In CoNLL, 2017.

Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal,
Heinrich K¨uttler, Mike Lewis, Wen-tau Yih, Tim Rockt¨aschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. In NeurIPS, 2020a.

Patrick Lewis, Pontus Stenetorp, and Sebastian Riedel. Question and answer test-train overlap in
open-domain question answering datasets. arXiv preprint arXiv:2008.02637, 2020b.

Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich K¨uttler, Aleksandra Piktus,
Pontus Stenetorp, and Sebastian Riedel. Paq: 65 million probably-asked questions and what you
can do with them. In EACL, 2021.

Yanyang Li, Ye Lin, Tong Xiao, and Jingbo Zhu. An efficient transformer decoder with compressed
sub-layers. arXiv preprint arXiv:2101.00542, 2021.

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike
Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining
approach. arXiv preprint arXiv:1907.11692, 2019.

Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh.
Entity-based knowledge conflicts in question answering. arXiv preprint arXiv:2109.05052, 2021.


-----

David Lopez-Paz and Marc’Aurelio Ranzato. Gradient episodic memory for continual learning. In
_NeurIPS, 2017._

Michael McCloskey and Neal J Cohen. Catastrophic interference in connectionist networks: The
sequential learning problem. Psychology of learning and motivation, 24:109–165, 1989.

David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild,
David So, Maud Texier, and Jeff Dean. Carbon emissions and large neural network training. arXiv
_preprint arXiv:2104.10350, 2021._

Fabio Petroni, Tim Rockt¨aschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller,
and Sebastian Riedel. Language models as knowledge bases? In EMNLP, 2019.

Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James
Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, et al. Kilt: a benchmark for knowledge intensive language tasks. In NAACL, 2021.

Nina Poerner, Ulli Waltinger, and Hinrich Sch¨utze. E-bert: Efficient-yet-effective entity embeddings
for bert. In Findings of EMNLP, 2019.

Ameya Prabhu, Philip HS Torr, and Puneet K Dokania. Gdumb: A simple approach that questions
our progress in continual learning. In ECCV, 2020.

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language
models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi
Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text
transformer. arXiv preprint arXiv:1910.10683, 2019.

Adam Roberts, Colin Raffel, and Noam Shazeer. How much knowledge can you pack into the
parameters of a language model? In EMNLP, 2020.

Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray
Kavukcuoglu, Razvan Pascanu, and Raia Hadsell. Progressive neural networks. arXiv preprint
_arXiv:1606.04671, 2016._

Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. AutoPrompt:
Eliciting knowledge from language models with automatically generated prompts. In EMNLP,
2020.

Fan-Keng Sun, Cheng-Hao Ho, and Hung-Yi Lee. Lamol: Language modeling for lifelong language
learning. In ICLR, 2020.

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. Fever: a large-scale
dataset for fact extraction and verification. In NAACL, 2018.

J¨org Tiedemann and Santhosh Thottingal. OPUS-MT — Building open translation services for the
World. In EAMT, Lisbon, Portugal, 2020.

Pat Verga, Haitian Sun, Livio Baldini Soares, and William W Cohen. Facts as experts: Adaptable
and interpretable neural memory over symbolic knowledge. In NAACL, 2021.

Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Simas Sakenis, Jason
Huang, Yaron Singer, and Stuart Shieber. Causal mediation analysis for interpreting neural nlp:
The case of gender bias. In NeurIPS, 2020.

Cunxiang Wang, Pai Liu, and Yue Zhang. Can generative pre-trained language models serve as
knowledge bases for closed-book qa? In ACL, 2021a.

Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Cuihong Cao, Daxin Jiang,
Ming Zhou, et al. K-adapter: Infusing knowledge into pre-trained models with adapters. In
_Findings of ACL, 2021b._


-----

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi,
Pierric Cistac, Tim Rault, R´emi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick
von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger,
Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Transformers: State-of-the-art natural
language processing. In EMNLP System Demonstrations, 2020.

Jing Xu, Arthur Szlam, and Jason Weston. Beyond goldfish memory: Long-term open-domain
conversation. arXiv preprint arXiv:2107.07567, 2021.

Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov,
and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question
answering. In EMNLP, 2018.

Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang. Lifelong learning with dynamically
expandable networks. In ICLR, 2018.

Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and
Yejin Choi. Defending against neural fake news. In NeurIPS, 2019.

Michael J.Q. Zhang and Eunsol Choi. SituatedQA: Incorporating extra-linguistic contexts into QA.
_EMNLP, 2021._

Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, and
Sanjiv Kumar. Modifying memories in transformer models. arXiv preprint arXiv:2012.00363,
2020.


-----

A EXTENSION OF RELATED WORKS

As mentioned in Section 2, there are fundamental differences between the traditional CL formulations and CKL which make the previous CL methods inadequate for the CKL setting. In this
section, we introduce the prior traditional continual learning methods in detail, explore the methods
from the literature set as baselines for the CKL benchmark and how they address the identified limitations of CL methods, and provide descriptions about alternative methods making LMs cope with
the changing world.

A.1 TRADITIONAL CONTINUAL LEARNING

Traditional continual learning (CL) methods focus on addressing two aspects of transfer between
sequentially incoming tasks: forward transfer and backward transfer (Lopez-Paz & Ranzato, 2017).
_Forward transfer refers to how past tasks affect the performance of the current and future tasks._
_Backward transfer refers to how current or future tasks affect the performance of previous tasks._
The general pretrain-finetune approach can be seen as an instance of positive forward transfer where
a model performs better on a target task after being pretrained on a more general source task. Moreover, catastrophic forgetting can be seen as an instance of negative backward transfer where previous
tasks suffer performance due to continued training on different tasks. With respect to these two aspects, CL approaches can be categorized into three main approaches: regularization, rehearsal, and
parameter-expansion methods.

**Regularization** Elastic Weight Consolidation (EWC) (Kirkpatrick et al., 2017) is a method that
regularizes important parameters of previous tasks while training for the current tasks, helping mitigate the negative backward transfer of previous tasks. Important parameters are measured via a
Fisher information matrix computed by measuring the magnitude of the gradient update step of each
parameter during training of previous tasks.

**Rehearsal** Gradient Episodic Memory (GEM) (Lopez-Paz & Ranzato, 2017) is one of the first
rehearsal methods that utilize samples from each task stored in episodic memory and places an
inequality constraint with respect to the losses of the samples in order to prevent negative backward
_transfer as well as allow the positive backward transfer. Other methods such as Experience replay_
and local adaptation (d’Autume et al., 2019) replay samples stored in the memory of previous tasks
during training to mitigate forgetting.

**Parameter-expansion** Progressive Neural Networks (PNN) (Rusu et al., 2016) is one of the earliest parameter-expansion/sharing approaches that introduce new sets of parameters for each new
task where previous parameters are frozen and can be connected via lateral connections allowing for
_positive forward transfer. PNN not only prevents negative backward transfer but also surpassed the_
previous pretrain-finetune approach in terms of positive forward transfer in some tasks.

A.2 CKL METHODS FOR LANGUAGE MODELS

As mentioned in Section 2, we explore the methods from the literature that have addressed the
limitations of CL methods and thus are applicable to CKL. We also categorize these methods into
the three main categories of CL.

**Regularization** Most CL methods that utilize regularization require computing important parameters of the previous task, which in this case is pretraining on the original text corpus. Determining
these parameters is oftentimes unrealistic since it requires large-scale pretraining which can hardly
be replicated by most. Also, exactly how and where the knowledge is stored in the parameters of an
LM is currently extremely difficult to identify and localize (Vig et al., 2020; De Cao et al., 2021).
RecAdam (Chen et al., 2020) overcomes this limitation by following the same training objective
as EWC (Kirkpatrick et al., 2017) with a stronger independent assumption and places a quadratic
penalty, ridding the need to access the initial pretraining corpus.


-----

**Rehearsal** Large LMs are usually pretrained on a vast amount of raw text corpus such as Common
Crawl[9]. When treating pretraining as a CL task, limitations exist when trying to apply previous
rehearsal methods since a few samples from the pretraining corpus cannot represent the overall
world knowledge from the original pretraining corpus. Mix-Review (He et al., 2021) solves this issue
by performing preliminary experiments in a smaller pretraining setting by assuming access to the
pretraining corpus during finetuning and mixing random subsets of pretraining corpus depending on
a mix-ratio that anneals towards the target task as training progresses. Mix-Review can be considered
a mild version of multi-task learning.

**Parameter-expansion** K-Adapter (Wang et al., 2021b) shares and freezes the original parameters and adds new parameters through adapters for continued pretraining of factual and linguistic knowledge and improve performance on three different knowledge-driven downstream tasks.
More recently, LoRA (Hu et al., 2021) freezes the original parameters and injects trainable rankdecomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters and the computational hardware requirement while performing on-par or
better than training all of the parameters. Both methods hypothesize freezing the original parameters
allows mitigation of catastrophic forgetting. We test out the hypothesis through implementation in
our CKL benchmark.

A.3 METHODS OF INTEGRATING WORLD KNOWLEDGE WITH LANGUAGE MODELS

**Explicit Methods** Facts-as-Experts (Verga et al., 2021) store representations of entities in the form
of key-value pairs into external memory that can be modified during inference time. RAG (Lewis
et al., 2020a) accesses a dense vector index of Wikipedia with a retriever and swaps indexes for
updating the behavior of the model as the world changes. Blender Bot 2.0 (Xu et al., 2021; Komeili
et al., 2021), is also one of the explicit methods that search the internet for recent knowledge and
saves recent conversations in external long-term memory. Explicit methods, such as swapping indexes, adding explicit entity-relation knowledge, or searching the internet are in need of manual
intervention during inference or are bound to tasks that require retrieval. In this paper, we focus
only on implicit methods.

**Implicit Methods** Zhu et al. (2020) proposed a new task of explicitly modifying specific facts
without forgetting unmodified facts and provided several benchmark approaches without utilizing
non-parametric memory, including constrained layer-wise finetuning. Wang et al. (2021b) proposed
K-Adapter, a method that adds adapters to frozen layers of pretrained LMs to inject factual and
linguistic knowledge and improve performance on downstream tasks. Chen et al. (2020) proposed a
new optimizer that simulates the pretraining optimization while finetuning on the target task without
needing access to the pretraining corpus, improving performance on the GLUE benchmark. De Cao
et al. (2021) propose using a hyper-network to edit factual knowledge.

Even though these implicit methods are efficient methods of injecting or modifying knowledge from
the implicit parameters of the LMs, they are all limited to injecting specific knowledge such as the
case of (Wang et al., 2021b) or modifying past knowledge such as the case of (Zhu et al., 2020;
De Cao et al., 2021). No work, to the best of our knowledge, has specifically addressed the catas_trophic forgetting of world knowledge gained from the initial pretraining when continued pretraining_
on new text corpus for the gain of new world knowledge.

B DATASET CONSTRUCTION

In this section, we describe the dataset construction process we undergo in creating the benchmark
datasets used in CKL. For the construction, we use Amazon Mechanical Turk (mturk)[10] for crowdsourcing Human Intelligent Tasks (HITs) and separately hire 11 experts for annotation that requires
extensive searching of the C4 corpus. In addition, three more experts[11] who set up the data construction process and prepared the annotation guideline to ensure the quality of the data through
post-validation and giving feedback to the annotators in real-time. The interfaces used for mturk
HITs are provided in Appendix B.2.

[9https://commoncrawl.org/](https://commoncrawl.org/)
[10https://www.mturk.com](https://www.mturk.com)
11The first three authors of the paper.


-----

_recent news articlerecent news article from CC- from_ _recent news articlerecent news article from CC- from_

CC-RecentNewsRecentNews CC-RecentNewsRecentNews

HIT

PAQ Question Generator haveDoes the have Does the newnew information? information ?news article news article no _disregard_

probably-asked questionsprobably-asked questions

yes

Select a question that requires recent knowledge to answer AND answer can be found in the article HIT Choose a sentence that conveys the most recent information

from the article. Then, select an entity from the sentence.

_question_ _new_answer = answer_

_sentence_ _answer = selected_word_

Convert to cloze sentence

_cloze sentence_ Decontextualization Model

Pretrained T5 LM Back-Translation Model

_prediction_ _disregard_ In-Lab Experts _decontextualized & paraphrased sentence_

no

_predictionIs the correct in C4?Is the correct in C4prediction  ?_ yes _prediction != new_answerpredictionnew_answer !=_ yes _evidence = evidence old_answer = predictionin C4_ _paraphrased sentence ?Is the paraphrased sentence Is the answeranswer still in still in ?_ no _disregard_

no _add to UpdatedLAMA_

_answer Can an Can an be found in C4 ?be found in C4?alternativealternativeanswer_ yes _alternative answer != new_answeralternative_answer!= new_answer_ yes _old_answer = alternative_ Convert to question and answer the questionyes HIT

_evidence = evidence in C4_

no no _add to UpdatedLAMA_ _answersanswers Do the Do the agreeagree?_ ? no _disregard_

_add to NewLAMA_ _disregard_

_add to NewLAMA-Easy_

(a) (b)


Figure 4: Dataset construction pipeline for (a) UPDATEDLAMA, NEWLAMA, and (b) NEWLAMA-EASY

**CC-RECENTNEWS** We first construct CC-RECENTNEWS, a novel text corpus containing relatively new knowledge as D1. We use news-please (Hamborg et al., 2017), similar to the CCNEWS (Liu et al., 2019) and REALNEWS dataset (Zellers et al., 2019), to crawl 221,779 news
articles published from May 2020 to April 2021. LMs initially pretrained on D0 constructed before May 2020 can be continually pretrained on CC-RECENTNEWS to gain relatively recent world
knowledge.

**INVARIANTLAMA** We create INVARIANTLAMA, a subset of the LAMA (Petroni et al., 2019)
task for measuring time-invariant knowledge which might be forgotten during CKL. Among the
41 relations of the T-REx (Elsahar et al., 2018) subset of LAMA, we manually select 28 relation
types that probe for time-invariant instances (a full list of time-invariant relations are provided in
Appendix B.1). We also remove instances where the answer overlapped with the subject following
Poerner et al. (2019) since the answers for these instances can be inferred from the cloze statement
itself. Lastly, we remove instances where the answer was a non-entity to leave only the instances
that require world knowledge for prediction on their answers (Guu et al., 2020).

**UPDATEDLAMA and NEWLAMA** We construct UPDATEDLAMA and NEWLAMA for measuring the update of outdated knowledge and acquisition of new knowledge during CKL. The challenge of constructing UPDATEDLAMA is that a knowledge instance can be only considered as the
knowledge that requires update only if it is present in both D0 and D1 with changed details, and
the challenge of constructing NEWLAMA is that the knowledge can be considered new only if it is
in D1 but not in D0. Therefore we set up the data construction process carefully. The pipeline for
the creation of a single instance of UPDATEDLAMA and NEWLAMA, is shown in Figure 4 (a).
Each potential instance starts off from a single article from CC-RECENTNEWS and goes through
the pipeline which will end up being (1) discarded (2) added to UPDATEDLAMA or (3) added to
NEWLAMA in the end. The procedure is as follows:

(1) First, a list of Probably-Asked Questions (Lewis et al., 2021) are generated using the PAQ question generator on a single news article from CC-RECENTNEWS. (2) The list of PAQs and the news
article is given to the crowd-sourced worker to select a question that asks for the most recent knowledge for which the answer (denoted as new answer) can be found in the article. (3) The crowd-source
worker is instructed to convert the question into a cloze sentence so that it can be given as input to
a pretrained T5 LM. The predictions of the T5 LM are stored along with the questions and cloze
sentences. (4) The expert annotator ensures the quality of the questions and cloze sentences by cor

-----

recting them whenever necessary and checks whether the model prediction is correct by searching
through the C4 corpus as a representative of D0[12]. If the prediction is correct and the prediction is
not the same with the new answer, the following instance must be present in both D0 and D1 with
details changed, and thus is added to UPDATEDLAMA along with the evidence document found in
C4. If same, the instance is discarded because the instance is neither updated nor new. (5) Lastly,
if the model prediction is wrong, the expert annotator is asked to find an alternative answer for the
question in C4. If not found, the instance is added to NEWLAMA since the answer to the question
could only be found in the article of CC-RECENTNEWS (D1), but not in C4 (D0). Similarly, if the
alternative answer is found in C4, we check whether it is the same as the new answer and add the
instance to UPDATEDLAMA if not the same and disregard it otherwise.

Throughout the whole process, a validator checks the sanity of the data and gives detailed real-time
feedback on the work of the annotator.

**NEWLAMA-EASY** Even though NEWLAMA corresponds to our exact definition of new knowl_edge that we define in the task formulation, scaling the size of the dataset was difficult since each_
instance required searching the whole C4 database for answers. Instead, we provide a much larger,
_easier variant NEWLAMA-EASY where we test the general new knowledge acquired during con-_
tinued pretraining on CC-RECENTNEWS. The pipeline for the creation of a single instance of
NEWLAMA-EASY is shown in Figure 4 (b) and follows the following procedures:

(1) First, the crowd-sourced worker is instructed to classify whether the given article contains new
information or not. (We define new as not likely to be known before May 2020). If the article contains new information, the worker is instructed to select a sentence from the article that contains the
most recent information and an entity among the possible answer candidates in the sentence and discard the article if otherwise. We provide the possible entities through a Named-Entity Recognition
Model. (2) We make the selected sentence stand-alone from the article through the decontextualization model provided by Choi et al. (2021). (3) The decontextualized sentence is paraphrased by
a back-translation model (en→de→en) (Tiedemann & Thottingal, 2020) and checked whether the
selected word is still in the paraphrased sentence; the sentence is discarded if not. (4) Next, we mask
out the selected word from the sentence and ask two crowd-sourced workers to convert the cloze
sentence into a question and answer the question. (5) If the answers agree among the workers as
well as correspond to the actual selected word, we add the instance to NEWLAMA-EASY.

The specific interfaces used for the mturk HITs are provided in Appendix B.2. Statistics of the
constructed datasets are in Appendix B.3.

B.1 TIME-INVARIANT RELATIONS OF LAMA

Table 4 shows the list of 28 time-invariant relations of INVARIANTLAMA. We manually filter the
44 original LAMA relations to leave only the time-invariant relations. Templates such as “[X] works
for [Y] .” and “[X] is a member of [Y] .” are excluded because the answer may change for different
timestamps. In the template, [X] and [Y] refers to subject and object labels, respectively. Given a
template with only the subject included, the model has to predict the object label [Y] for knowledge
probing.

B.2 INTERFACES USED FOR THE CONSTRUCTION OF CKL BENCHMARK

The Mturk interface used during construction of UPDATEDLAMA and NEWLAMA, NEWLAMAEASY, and NEWLAMA-EASY are shown in Figure 5, 6, and 7, respectively.

B.3 DATASET STATISTICS AND EXAMPLES

We report the data statistics for the CKL benchmark in Table 5. We measure the size, average input
token length, average answer token length, and the answer types of each constructed dataset. One
thing to consider is that LAMA (Petroni et al., 2019) from which we constructed INVARIANTLAMA
is originally constructed for only single-token decoding (1.3 with the T5-tokenizer) because multitoken decoding entails additional, tunable parameters (beam size, n-gram repetition penalties, etc.).

[12The expert annotators are instructed to use https://c4-search.apps.allenai.org/ for searching through the C4](https://c4-search.apps.allenai.org/)
corpus.


-----

Table 4: Relations of INVARIANTLAMA

**Relation** **Template ([X], [Y])** **Example**

P19 [X] was born in [Y] . Taras Kuzio was born in Halifax .
P20 [X] died in [Y] . Georgios Roilos died in Athens.
P279 [X] is a subclass of [Y]. Hutterite German is a subclass of Bavarian .
P37 The official language of [X] is [Y]. The official language of Azad Kashmir is English .
P449 [X] was originally aired on [Y] . Microsoap was originally aired on BBC.
P47 [X] shares border with [Y] . Illinois shares border with Kentucky .
P138 [X] is named after [Y] . Logan International Airport is named after Boston .
P364 The original language of [X] is [Y] . The original language of The Fatal Eggs is Russian .
P527 [X] consists of [Y] . AIM alliance consists of Apple .
P176 [X] is produced by [Y] . Alfa Romeo 155 is produced by Fiat .
P27 [X] is [Y] citizen . Woodrow Lloyd is Canada citizen .
P407 [X] was written in [Y] . France Culture was written in French .
P30 [X] is located in [Y] . Lavoisier Island is located in Antarctica .
P178 [X] is developed by [Y]. Tizen is developed by Intel .
P1376 [X] is the capital of [Y], London is the capital of England .
P131 [X] is located in [Y] . Pershing County is located in Nevada .
P1412 [X] used to communicate in [Y]. Jacques Rivette used to communicate in French .
P17 [X] is located in [Y] . Eibenstock is located in Germany .
P276 [X] is located in [Y] . Delhi Technological University is located in India .
P937 [X] used to work in [Y]. Pierre Trudeau used to work in Ottawa .
P140 [X] is affiliated with the [Y] religion . Emirate of Granada is affiliated with the Islam religion .
P103 The native language of [X] is [Y] . The native language of Anastasy Vonsyatsky is Russian .
P190 [X] and [Y] are twin cities . Beijing and Milan are twin cities .
P1001 [X] is a legal term in [Y] . Surgeon General is a legal term in Canada .
P495 [X] was created in [Y] . La Grande Vadrouille was created in France .
P36 The capital of [X] is [Y] . The capital of Granville County is Oxford .
P740 [X] was founded in [Y]. Grimaldi Group was founded in Naples .
P361 [X] is part of [Y] . Sinqa is part of Andes .

Table 5: CKL benchmark dataset statistics

**Avg. Input** **Avg. Answer**
**Dataset** **Size** **Answer Types**
**Token #** **Token #**

Geographical (54%), Language (14.9%), Nationalities (7.2%)
INVARIANTLAMA 17474 11.9 1.3
Person (6.3%), Location (5.7%), Organization (5.3%), etc. (6.6%)

Person (61.47%), Organization (8.3%), Geographical (6.6%),
UPDATEDLAMA 924 13.7 9.4
Numerals (5.19%), Date (2.4%), etc. (16.04%)

Person (59.7%), Organization (10.2%), Numerals (7.6%)
NEWLAMA 797 14.7 8.7
Date (5.3%), Geographical (4.8%), etc. (12.4%)

NEWLAMA- Person (48.5%), Organization (13%), Geographical (9.8%)
11177 44.4 6.1
EASY Date (5.5%), Nationalities (3.4%), Numerals (2.5%), etc. (17.3%)

The newly constructed datasets UPDATEDLAMA, NEWLAMA, and NEWLAMA-EASY require
multi-token decoding which adds a level of difficulty for the task compared to INVARIANTLAMA.
Moreover, NEWLAMA-EASY has a different input distribution (longer input sequences) than the
other datasets since the decontextualization and back-translation processes are applied to create each
instance, which makes the sentences longer. Lastly, some examples of the CKL benchmark datasets
are provided in Table 6.


-----

Figure 5: Mturk interface used for construction of UPDATEDLAMA and NEWLAMA

Figure 6: First mturk interface used for construction of NEWLAMA-EASY

Figure 7: Second mturk interface used for construction of NEWLAMA-EASY


-----

Table 6: Examples of INVARIANTLAMA, UPDATEDLAMA, NEWLAMA, and NEWLAMA-EASY

|Task|Input|Output|
|---|---|---|


|INVARIANTLAMA|iPod Touch is produced by . The Sharon Cuneta Show was created in . The native language of Lee Chang-dong is .|Apple Philippines Korean|
|---|---|---|


|UPDATEDLAMA|is the prime minister of England. has the most passing yards in the NFL. Bale has champions league titles with Real Madrid.|Theresa May→ Boris Johnson Brady Quinn→ Jalen Guyton 3→4|
|---|---|---|


|NEWLAMA|Alicia Braga plays in the New Mutant. owns the rights to the Falcon and the Winter Soldier. Tesla invested in the digital currency bitcoin.|Cecilia Reyes Disney 1.5 billion|
|---|---|---|


|NEWLAMA-EASY|The decision of the two volleyball stars Bria and Cimone Woodard to withdraw from the Power 5 School to study at has become a national story. Allen Lazard is officially listed as questionable with a nuclear injury after missing the last games.|Howard University six|
|---|---|---|



C EXPERIMENTAL CONFIGURATION

**Pretraining Congifuration** We utilize the T5 initially pretrained on C4 (April 2019) and continually pretrained with salient span masking (Guu et al., 2020) on Wikipedia (May 2020) as initialization. We use the checkpoints from Wolf et al. (2020). We also perform the SSM objective during
CKL because it was shown to help LMs “focus on problems that require world knowledge” (Guu
et al., 2020; Roberts et al., 2020).

**Continual Pretraining Configurations** The input and output sequence length is fixed to 350.
We use gradient accumulation for cases where the same number of training batches could not be
loaded on the GPUs due to the varying memory consumption required for different methods and
set the global batch size to 60. We use Adafactor optimizer with an initial learning rate of 1e-3.
We show the effects of learning rate variation regarding the trade-off between maintaining previous
knowledge and acquiring new knowledge in Appendix E. We use learning rate warm-up for the first
10% of training and linearly decay the learning rate to half of the initial learning rate towards the end
of training. For all of the experiments, we use 4 32GB V100 GPUs for training with each method
except Mix-Review, where we use 16 32GB V100 GPUs. The details of the configurations used for
evaluation on each individual CKL task are provided in Appendix C.

**Evaluation Configurations** For T5 based models, all evaluation is done in a zero-shot manner and
is processed with a single GPU. For INVARIANTLAMA, the input and output length is fixed as 25
and 4 respectively. For UPDATEDLAMA and NEWLAMA, the input and output length is 50 and 10
respectively. Lastly, the input and output length is 150 and 10 respectively for NEWLAMA-EASY.
The rationale of this hyperparameter is based on average input and answer token in Table 5.

Unlike T5 models, GPT-2 based models need additional light-tuning for 1 epoch for evaluation. For
INVARIANTLAMA, the input and output length is 50 and 3 respectively. The training batch size is
32 and the learning rate is 1e-3. For evaluation on the acquisition of new knowledge, the input and
output length is 100 and 10 respectively. The training batch size is 8 due to memory constraints and
the learning rate is 1e-3. For both tuning processes, 4 V100 32GB GPUs are used. The detailed
result and discussion of GPT-2 based models are shown in Appendix G.

D HYPERPARAMETERS FOR IMPLEMENTATION OF CKL METHODS

**RecAdam (Chen et al., 2020) We use the same hyperparameter setting for the optimizer as in Chen**
et al. (2020): we set the coefficient of the quadratic penalty γ to 5,000, and select the best t0 and k in
100, 250, 500, 1,000 and 0.05, 0.1, 0.2, 0.5, 1 respectively for the annealing coefficient λ (t).


-----

Table 7: Result of T5-Vanilla and T5-Kadapters continually pretrained with various learning rates. The experiments are done under the setting of SMALL scenario in Table 3, thus D0 are C4 (April 2019) and Wikipedia
(May 2020), and D1 is CC-RECENTNEWS-SMALL. Each of IL and NLE stands for INVARIANTLAMA and
NEWLAMA-EASY. The parameters of FUAR are T[F], T1[U] [, and][ T]1[ A][, the tasks measuring the amount of time-]
invariant knowledge from corpus D0, updated knowledge from D1, and newly acquired knowledge from D1,
respectively.

**IL** **NLE** **FUAR**
**Method** **Learning Rate** EM EM ((IL), **_n.d.,_** **NLE) ↓**

T5-Initial -  **24.17** 8.9 - 

T5-Vanilla 1e-05 19.15 13.56 1.08
T5-Vanilla 1e-04 17.45 15.21 1.06
T5-Vanilla 5e-04 14.88 15.89 1.33
T5-Vanilla 1e-03 11.19 18.77 1.32

T5-Kadapters (k=2) 1e-04 19.93 14.93 **0.70**
T5-Kadapters (k=2) 1e-03 16.46 **19.59** 0.72

**Mix-Review (He et al., 2021) We use the English Wikipedia** [13] to represent the original pretraining
corpus. The mix-decay and mix-ratio are set to 4 and 0.7, respectively, which is the best hyperparameter setting in the paper.

**LoRA (Hu et al., 2021) We only freeze the encoder for the encoder-decoder LM and the entire**
model for the decoder-only LM. We use the optimal rank r of 4 and adapt both Wq and Wv in the
self-attention module, which corresponds to the best performing hyperparameter setting in the paper.

**K-Adapter (Wang et al., 2021b) Similarly with T5-LoRA, we freeze the encoder for the encoder-**
decoder LM and the entire model for GPT-2. We implement k = 2, 3 for both T5 and GPT-2 to
see the effect of increasing # of parameters. Unlike in the original paper, we set the configuration
of the adapter identical to a single transformer layer from the original LM, ridding the need of an
up-projection and down-projection layer.

**Modular We use a projection layer before adding the hidden state outputs from both encoders to**
match the dimensions.

**Why do we add parameters to only the encoder for T5? For parameter-expansion methods, we**
add parameters to only the encoder because the encoder is applied to the input sequence and the
decoder is applied to the output sequence. Since most of the computational cost comes from the
decoder computing for the output sequence in an auto-regressive manner as highlighted in (Li et al.,
2021), the newly added parameters in the encoder are roughly expected to have minimal additional
computational cost.

**Why do we freeze parameters of only the encoder for T5? K-Adapter and LoRA are initially**
proposed to freeze all of the parameters except for the newly added parameters. However, when
applying this methodology to T5, it was empirically shown that unfreezing the parameters of the
decoder results in better performances when utilized together with parameter-expansion methods in
terms of overall trade-off.

E EXPLORING THE TRADE-OFF OF VARYING THE LEARNING RATE FOR
CONTINUAL PRETRAINING

Table 7 shows that lowering the learning rate for the continual pretraining leads to less forgetting of
the original knowledge, but also less learning of new knowledge. The experiments are done under
the setting of SMALL scenario in Table 3.

By comparing the FUAR among the T5-Vanilla models with different learning rates, it can be seen
that there is no rule of thumb for choosing the appropriate learning rate since FUAR is the lowest

[13https://huggingface.co/datasets/wikipedia](https://huggingface.co/datasets/wikipedia)


-----

Table 8: Dev performance on KILT benchmark datasets after finetuning. Each model is finetuned on the train
sets of KILT after continually trained on CC-RECENTNEWS dataset for 4 epochs.

Fact Checking Entity Linking Slot-filling Open Domain QA Dialogue


**Method**


**FEVER** **AY2** **WnWi WnCw T-REx zsRE** **NQ** **HoPo TQA** **ELI5** **WoW**

ACC ACC ACC ACC ACC ACC EM EM EM Rouge F1


T5-Initial 80.39 81.44 **50.47** **48.92** 44.64 **4.40** 25.63 17.64 **28.38** 13.46 13.92

T5-Vanilla 78.02 81.19 48.17 46.46 44.08 2.04 24.93 14.36 26.51 13.38 13.07
T5-RecAdam 77.83 81.44 49.12 47.01 43.04 2.58 24.65 14.86 25.99 13.71 12.69
T5-MixReview 77.17 80.77 49.38 46.22 44.08 2.47 25.07 14.57 26.36 13.57 12.73
T5-LoRA 79.89 81.44 48.82 47.29 45.68 3.01 25.49 16.71 28.23 13.42 13.60
T5-Kadapters (k=2) 80.35 80.94 48.91 46.65 45.52 3.33 **26.20** 16.57 26.89 13.15 12.94
T5-Kadapters (k=3) 80.31 80.52 47.09 46.26 45.60 3.12 24.79 16.57 25.62 **13.82** 13.42
T5-Modular **80.54** **82.44** 48.44 44.81 **48.16** 3.44 24.51 **18.43** 28.31 13.72 **14.03**

Table 9: Hyperparameters and dataset details for all tasks of KILT.

Fact Checking Entity Linking Slot-filling Open Domain QA Dialogue

**FEV** **AY2** **WnWi** **WnCw** **T-REx** **zsRE** **NQ** **HoPo** **TQA** **ELI5** **WoW**

Epoch 5 20 -  -  9 30 45 12 50 6 8
Input Seq 25 768 512 2,048 25 25 35 50 25 35 175
Output Seq 10 6 6 6 6 6 6 8 10 350 40
LR 1e-4 1e-4 -  -  1e-3 1e-4 1e-3 1e-4 1e-3 1e-3 1e-4
Batch Size 128 16 128 48 512 256 256 256 128 32 64
Train Size 104,966 18,395 -  -  2,284,168 147,909 87,372 88,869 61,844 272,634 63,734
Dev Size 10,444 4,784 3,396 5,599 5,000 3,724 2,837 5,600 5,359 1,507 3,054

in learning rate of 1e-4 and increases for both lower and higher learning rates. We suppose that the
optimal learning rate heavily depends on the corpus size of D1 and the model capacity of LM. We
also report the performance of T5-Kadapters, which is a CKL method that shows robust performance
throughout most experiments. Applying T5-Kadapters consistently mitigates the trade-off between
forgetting and acquiring new knowledge as shown by the improvement in FUAR from the T5-Vanilla
model with the same learning rates, although the level of effectiveness varies according to the value
of the learning rate. We do not perform extensive experiments with each of the varying learning
rates since searching for the optimal learning rate for each different continued pretraining setting
may be out-of-scope with this research.

F EXPLORING HOW CONTINUALLY PRETRAINING ON D1 AFFECTS KILT
TASKS WHICH REQUIRES KNOWLEDGE FROM D0

In addition to the CKL benchmark, we also show in Table 8 the performance on the dev set of
KILT (Petroni et al., 2021) after finetuning each of the continually pretrained models of Table 2.
Since KILT is made from Wikipedia, which corresponds to the old pretraining corpus D0, the performance on KILT measures how continual pretraining on new corpus D1 affects the performance
on the knowledge obtained from D0 if finetuning is done on behalf of the knowledge from D0.

**Configuration** KILT (Petroni et al., 2021) consists of 5 different tasks and 11 datasets: OpenDomain Question Answering (Joshi et al., 2017; Kwiatkowski et al., 2019; Fan et al., 2019; Yang
et al., 2018), Fact Checking (Thorne et al., 2018), Entity Linking (Hoffart et al., 2011; Guo &
Barbosa, 2018), Slot-filling (Levy et al., 2017), and Knowledgeable Open Dialogue (Dinan et al.,
2019). Because each task requires a different training objective than the one used during pretraining,
additional finetuning is necessary. We search for the hyperparameters such as training epochs, batch
size, input size, output size, and learning rate of each individual KILT task to match the T5-base
dev performance reported by Petroni et al. (2021). Using the identified configurations, we perform
experiments on all of the KILT tasks with the continually pretrained models for each method as the
initialization checkpoints. Evaluation metrics are different for each dataset: accuracy for discrete


-----

output (fact-checking, entity linking, slot-filling), Exact Match (EM) for question answering tasks
with short output, ROUGE-L for ELI5 (question answering task with long output), and F1-score for
Wizard of Wikipedia (dialogue). The data statistics and the hyperparameters used for finetuning on
each KILT dataset is reported in Table 9.

**Experimental Result** We first focus on the performance on zero-shot Relation Extraction (zsRE),
which is measured on the dev set of 12 relations that are ensured to have no overlap with the 84 relations of the train set (Levy et al., 2017). Since the setting is similar to the zero-shot probing setting
of IL, the trend of the result on the two datasets are similar. The performance of T5-Vanilla drops to
half from that of T5-Initial as shown in IL, and the best performing method for both datasets is T5Modular. In addition, corresponding with results from the CKL benchmark, parameter-expansion
methods generally show stronger performance than the other methods.

However, for the other datasets that cannot be performed in a zero-shot manner, the intermediate
process of continually pretraining on corpus D1 does not seem to be that harmful on the finetuning
for the target tasks even though they are more related to the knowledge of D0. Even T5-Vanilla
shows modest performance, sometimes with better results than some other CKL baselines. One
hypothesis is that the models could have regained the original knowledge from corpus D0 through
the finetuning process. Also, some of the knowledge could have been recovered through the testtrain overlap (Lewis et al., 2020b; Wang et al., 2021a).

A more surprising finding is that the performance of some of the parameter-expansion methods are
even higher than that of T5-Initial, which is considered to be the upper bound for KILT because T5Initial is only trained on behalf of the knowledge from D0. For example, T5-Modular shows higher
scores than T5-Initial on 6 out of 11 tasks. Since the parameter-expansion methods force the model
to store the new knowledge in the newly added parameters during continual pretraining, one careful
conjecture is these LMs have learned to combine and utilize in its internal representation of both old
and new knowledge stored in separate parameters during finetuning to maximize the performance.

G EXPLORING HOW CKL METHODS TRANSFER ACROSS LM
ARCHITECTURES

We perform experiments with GPT-2 Large ([∼] 774M params) (Radford et al., 2019) initially pretrained on WebText and Wikipedia[14] (D0) and continually trained on CC-RECENTNEWS-SMALL,
i.e., SMALL (D1) for 8 epochs. For continued pretraining, we use the common teacher-forcing pretraining objective. The initial learning rate for the continued pretraining stage is empirically chosen
as 1e-4 (results with learning rate as 1e-3 are shown in Appendix G.1). After continued pretraining,
we apply light-tuning, a process denoted for finetuning the model for only one epoch on a small
portion of data similar to the evaluation set. Training on a single epoch constrains the model to
barely adapt to the input-output form of the data and not to learn the knowledge in tuning samples,
mitigating the problem suggested by Lewis et al. (2020b).

To measure the time-invariant knowledge, we use InvariantLAMA (IL) because most of the slots to
fill are at the end of the sentence. For light-tuning on behalf of IL, we use additional T-Rex data from
Shin et al. (2020) which has a similar distribution as instances from IL. Among them, 5,000 instances
with the same time-invariant relations as IL are randomly sampled for light-tuning. On the other
hand, unlike IL where most of the slots to fill are at the end of the sentences, the LAMA datasets for
new knowledge in our CKL benchmark mostly have the slots at the beginning of the sentences.
Therefore, we use the corresponding CBQA dataset of NEWLAMA-EASY, NEWQUESTIONSEASY (NQE) to roughly measure the new knowledge.[15] For light-tuning on behalf of NQE, 5,000
instances are sampled from a set of QA pairs constructed from CC-RECENTNEWS but not CCRECENTNEWS-SMALL to remove the test-train overlap.

14GPT-2 was initially pretrained on WebText (Dec 2019), which consists of 8 million documents with
Wikipedia pages excluded. In order to measure the performance on INVARIANTLAMA constructed from
Wikipedia, we continually pretrain GPT-2 on a subset of Wikipedia (May 2020) for 14k global training steps
before CKL.
15The QA version of UL, NL and NLE will be also released with the main CKL benchmark.


-----

Table 10: Performance of decoder-only models initially pretrained on Dec 2019 dump of Webtext and May
2020 dump of Wikipedia (D0) continually pretrained on CC-RECENTNEWS-SMALL (D1) for 8 epochs with a
learning rate of 1e-4. Each of IL and NQE stands for INVARIANTLAMA and NEWQUESTIONS-EASY. The
parameters of FUAR are T[F], T1[U] [, and][ T]1[ A][, the tasks measuring the amount of time-invariant knowledge from]
corpus D0, updated knowledge from D1, and newly acquired knowledge from D1, respectively.

**IL** **NQE** **FUAR**
**Method** EM EM ((IL), **_n.d.,_** **NQE) ↓**

GPT2-Initial 38.11 4.3 - 

GPT2-Vanilla 35.88 5.79 1.58
GPT2-Recadam 35.50 5.79 1.84
GPT2-Mixreview **38.93** 5.57 **0**
GPT2-Lora 37.99 6.23 0.06

GPT2-Kadapters (k=2) 37.85 **6.34** 0.13
GPT2-Kadapters (k=3) 38.03 5.79 0.06

Table 10 shows the CKL benchmark performance of GPT-2 models. We report the results averaged over 5 runs with different random seeds. As in Table 2, parameter-expansion methods show
robust performance on both IL and NQE, resulting in low FUAR. This shows that these methods
are not only effective on the encoder-decoder model but also the decoder-only model as well. One
interesting result in Table 10 is that GPT2-MixReview performs the best on IL, with performance
even higher than the initial model, which results in the best FUAR of 0 which means no forgetting
occurred at all. We suppose that the training strategy of GPT2-MixReview, allowing access to samples of D0 during continued pretraining, would have allowed fast adaptation to knowledge from D0
during the light-tuning phase. Performance of GPT2-MixReview suggests that it makes it possible
to regain the original knowledge for decoder-only models even with small tuning steps.

We want to highlight that the discrepancy of the performances among the CKL methods between
encoder-decoder LM (T5) and decoder-only LM (GPT-2) may not solely be on the LM architecture,
but also on the learning rate and the evaluation method (light-tuning was used to evaluate GPT2 while we evaluated T5 in a zero-shot manner). We leave further exploration of training everchanging decoder-only LMs such as GPT-2 as future work.

G.1 FAILED GPT-2 EXPERIMENTS WITH LARGER LEARNING RATE

Table 11 shows the CKL benchmark result of GPT-2 models continually pretrained on CCRECENTNEWS-SMALL for 8 epochs with a learning rate of 1e-3. By comparing the results in this
table with those in Table 10, which is for models continually pretrained with a learning rate of 1e-4,
the results in Table 11 shows worse performance on both IL and NQE. Unlike in Appendix E, increasing the learning rate does not result in better learning of new knowledge. Instead, NQE performance is even worse than GPT2-Initial for GPT2-Vanilla, GPT2-Recadam, and GPT2-MixReview.
FUAR is no gain for these cases by the definition of the metric because the denominator has the
value of zero. This shows that a large learning rate for continual pretraining may lead to failure:
neither retaining old knowledge nor acquiring new knowledge effectively. For parameter-expansion
methods, because many parameters including the decoder are frozen during the continual training
process, they seem to be less prone to the effect of a large learning rate.

H EXPLORING THE PREDICTION CHANGE DURING CONTINUAL
PRETRAINING

Table 12 shows the prediction results of T5-Vanilla and T5-Modular on three knowledge probing
tasks: INVARIANTLAMA, UPDATEDLAMA, and NEWLAMA. We show the prediction for every
training epoch for each model. The instances are selected from the predictions that T5-Modular got
correct but T5-Initial got wrong on the final prediction, in order to see where the gap of the EM
comes from.


-----

Table 11: Performance of decoder-only models initially pretrained on Dec 2019 dump of Webtext and May
2020 dump of Wikipedia (D0) continually pretrained on CC-RECENTNEWS-SMALL (D1) for 8 epochs with a
learning rate of 1e-3. These are the results failed due to a large learning rate. Each of IL and NQE stands for
INVARIANTLAMA and NEWQUESTIONS-EASY.

**IL** **NQE** **FUAR**
**Method** EM EM ((IL), **_n.d.,_** **NQE) ↓**

GPT2-Initial **38.11** 4.37 - 

GPT2-Vanilla 23.03 1.64 _no gain_
GPT2-Recadam 25.38 2.73 _no gain_
GPT2-Mixreview 32.07 1.64 _no gain_
GPT2-Lora 34.52 5.46 3.29
GPT2-Kadapters (k=2) 33.67 6.01 2.71

GPT2-Kadapters (k=3) 31.75 **7.65** **1.94**

Table 12: Change of Prediction Outputs During Continued Pretraininig

**Cloze Sentence** **Model** **Epoch 1** **Epoch 2** **Epoch 3** **Epoch 4** **Answer**

The native language of V French French Khmer Malaya
French
Yvonne Monlaur is . M French French French French

V Sonic D Sonic the Sonic Found Sonic the
Sonic Drift 2 is developed by . Sega
M Sonic R Sega Sega Sega


V Microsoft Google GitHub Google
WebKit is developed by . Apple
M Apple Apple Apple Apple

The official language of Republic of V Russian English Kazakh English
Russian
Ingushetia is . M Russian Russian Russian Russian

V Rome Rome Constantino Constantino
The capital of Roman Empire is . Rome
M Rome Rome Rome Rome

The biggest exporter of crude oil V Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia →
to china is . M Russia Saudi Arabia Russia Russia Russia

is the head of V Mario Draghi Yves Le Maire Yves Dujarric Mario Draghi Mario Draghi →
the euro zone central bank M Mario Draghi Christine Lagarde Christine Lagarde Christine Lagarde Christine Lagarde

is the manager of V Mauricio Fernandez Steve Bruce Frank Lampard Mikel Arteta Luis Enrique →
chelsea in the premier league M Jose Mourinho Jose Mourinho Frank Lampard Frank Lampard Frank Lampard

V What 999 £1.25m £1.25m 36,000 →
is the price for a flat in nottingham
M This 30,000 pounds 40,000 pounds 40,000 40,000

was the governor of New York V Andrew M. Cuomo Cuomo Andrew Cuomo Franklin D. Roosevelt Martin Van Buren →
at the time this article was written M Andrew Cuomo Andrew Cuomo Andrew M. Cuomo Andrew Cuomo Andrew Cuomo

V Corey Williams Corey Connor
is on the Bills all-pro team Williams
M Williams Williams Williams Williams

is the founder of the popular V Satoshi Nakamoto Satoshi Nakamoto Yuri Xiaobo
Satoshi Nakamoto
cryptocurrency bitcoin M Vitalik Buterin Satoshi Nakamoto Satoshi Nakamoto Satoshi Nakamoto

V Rs. 1 crore a whopping $1 million $2 million $1 million
The bail for kyle rittenhouse is . $2 million
M $2 million $2 million $2 million $2 million


**IL**

**UL**

**NL**


The las vegas raiders beat V the Las Vegas Raiders the New Orleans Saints the Las Vegas Raiders the sacramento

the New Orleans Saints
in the playoffs M the New Orleans Saints the Kansas City Chiefs the Kansas City Chiefs the New Orleans Saints

V Yves samantha s Norma Mike
is the host of ellen de generes show Ellen DeGeneres
M Elise Ellen DeGeneres Ellen deGenes Ellen DeGeneres

I EXPLORING THE CAUSE OF THE EM GAP BETWEEN UPDATEDLAMA AND
NEWLAMA

As shown in the main experiment, Table 2, there is a considerable gap between the EM of UPDATEDLAMA (UL) and NEWLAMA (NL) over all the methods, despite undergoing the same data
construction process. We attempt to analyze the causation by first analyzing what answer types
make up the EM score of both UL and NL of T5-Vanilla, which are 10.17 and 3.77, respectively.
As shown in Figure 8a, the cloze sentences that take Person type as the ground truth makes up most
of the EM of both tasks, despite Person type answers taking up a similar proportion out of the total
answer types (61.46% for UL and 59.7% for NL). Since UL consists of probes requiring an update
of information from D0, one might conjecture that the EM gap is simply due to the difference of the


-----

UpdatedLAMA NewLAMA

24

18

EM 12

6

0

Person (Overlapping Answers)


(b) EM measured using the instances from UL and NL with
overlapping Person type answers


Person Organization Numerals

Geographical etc.

12

9

EM 6

3

0

UpdatedLAMA NewLAMA


(a) Composition of ground truth categories
of the correctly predicted instances


Figure 8: Analyzing the cause of the EM gap between UPDATEDLAMA and NEWLAMA.

Table 13: F1 Score of Main Results.

**IL** **UL** **NL** **NLE**
**FUAR**
**Method**
EM EM EM EM ((IL), **UL,** **NL) ↓**

T5-Initial **24.88** 2.62 3.19 14.49 - 

T5-Vanilla 13.11 11.89 5.84 22.53 0.68
T5-RecAdam 13.39 14.33 6.15 22.68 0.57
T5-MixReview 14.09 8.11 4.80 18.89 1.10
T5-LoRA 17.04 14.50 7.45 24.59 0.36
T5-Kadapters (k=2) 19.88 13.67 7.43 24.04 0.22
T5-Kadapters (k=3) 19.91 14.31 6.55 23.33 0.21

T5-Modular 21.35 12.78 6.94 24.42 **0.17**

frequency in each corpus of the entities that serve as the ground truths, e.g., those entities appear
more in corpus D0 than in D1. In order to get rid of the influence of frequency of entities when
analyzing the source of the EM gap, we find overlapping Person type answers from UL and NL,
and analyze only the 67 probing sentences for both datasets each paired to one of these entities. As
shown in Figure 8b, the EM on UL is still much higher than that of NL. Manually analyzing these
instances, we find that the probing sentences for NL ask for relatively more fine-grained knowledge
compared to UL, since the instances of UL by definition are overlapped cloze sentences with different answers in the corpus D0 and D1, that naturally make them be coarse-grained. For instance, the
probing sentences for entity “Tim Walz” in UL and NL are “ is the governor of Minnesota
this year.” and “ is the governor of Minnesota calling for the evacuation of St. Paul.”, respectively. We thus conjecture that the main causation of the EM gap to be UL consisting of instances
requiring coarse-grained knowledge, which is likely to have appeared more during in D1, while NL
consisting of instances requiring fine-grained knowledge, which is expected to likely have appeared
less in D1.

J ADDITIONAL ANALYSIS OF MAIN RESULTS


-----

T5-Initial T5-Baseline T5-RecAdam T5-MixReview

T5-LoRA T5-Kadapters (k=2) T5-Kadapters(k=3) T5-Modular

70 40 30 50

60 32 25 40

5040 24 20 30

15

mean P@k 3020 mean P@k 16 mean P@k 10 mean P@k 20

10 8 5 10

0 0 0 0

0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100

k k k k


(a) INVARIANTLAMA


(b) UPDATEDLAMA


(c) NEWLAMA


(d) NEWLAMA-EASY


Figure 9: Mean P@k curve for CKL benchmark with varying k.


-----

