Question:
According to the paper, which factors influence whether retrieval augmentation improves language model performance?
A. Popularity of the entity-relation pairs
B. The size of the language model
C. The presence of multi-hop reasoning in the query
D. The effectiveness of the retriever in retrieving relevant information

Answer: ABD

How does popularity affect RALM performance?
Figure 7 shows the accuracy of GPT-3.5 on questions with different subject-relation (S-R) counts. Notably, the accuracy of Vanilla and GenRead is considerable for popular questions but significantly declines when S-R counts drop below 10. This drop is likely due to models memorizing popular facts. Conversely, RALMs utilizing BM25, Contriever, GTR, and BGE exhibit greater robustness on less popular questions. However, for popular questions, their performance is inferior compared to the Vanilla model without augmentation. Given that supporting passages (Oracle) consistently enhance the performance of models, we hypothesize that retrieval errors for popular questions negatively impact the performance of RALMs using BM25, Contriever, GTR, and BGE.

Is there a correlation between RALM performance and retrieval errors?
To verify the above hypothesis, we closely examine the relationship between RALM performance and retrieval errors for questions of varying popularity. To estimate retriever performance, we compute passage ac-curacy, marking a passage as correct if any sub-string of the passage is an exact match for a gold answer. We report the passage accuracy of different retrieval models in Figure 8. This trend aligns closely with RALM performance, as illus-trated in Figure 7. Specifically, agreement ratios between RALM performance and passage accu-racy are 85.5%, 82.4%, 88.5%, 88.4%, 88.7%, and 93.1% for BM25, Contriever, GTR-large, GTR-xl, BGE, and Oracle, respectively. Upon closer exami-nation, we find that although retriever accuracy is low for rare questions, the drop is less significant compared to Vanilla LMs (see the leftmost plots in Figures 7 and 8). This indicates that retrieval aug-mentation is still beneficial for rare facts compared to recall. In contrast, Vanilla LMs perform better than retrievers for popular questions.

How does model size affect the need for augmen-tation?
We further explore how the size of the model influences the need for augmentation based on the popularity of questions, considering that the ability to recall varies with model size. To this end, we categorize the questions into four groups based on the median of S-R counts (5) and S counts (12):
• head-head: both S-R and S counts are high
• head-tail: S-R count is high but S count is low
• tail-head: S-R count is low and S count is high
• tail-tail: both counts are low
Figure 9 shows the accuracy of various models along with BM25, Contriver, GTR, and BGE pas-sage accuracy across the four groups. Note that we plot QA accuracy and passage accuracy since we use the same criteria for them. We observe that supervised retrievers, GTR and BGE, outperform vanilla LMs in all groups. This is attributed to the fact that it was fine-tuned with QA datasets about Wikipedia. For the generalizability to broader do-mains, we discuss the results with an unsupervised retriever BM25 in the following.9

In the head-head group, medium and large mod-els exhibit superior performance even without aug-mentation, e.g., the GPT series achieve higher than 80% accuracy. In contrast, there is a notable gap between the accuracy of the vanilla model and pas-sage accuracy for small models. This suggests that only small models need augmentation for questions in this group.

In the head-tail and tail-head groups, Llama and GPT series achieve comparable or superior accu-racy to the passage accuracy. This indicates that large models can memorize long-tailed relations of popular entities and popular relations of infre-quent entities. On the other hand, medium-scale models fail to recall compared to passage accu-racy, and hence, they will benefit from augmenta-tion. Interestingly, the passage accuracy in the tail-head group is significantly lower (less than 60%) compared to other groups. This retrieval error is caused by frequent mentions of the entity but in-frequent mentions of subject-relation pairs. Con-sequently, accurately identifying relevant passages from a large pool of passages containing the ques-tion entity becomes challenging.

In the tail-tail group, even GPT series struggle to recall long-tail information for rare entities. Since retrievers are robust for such information, we con-clude that retrieval augmentation is generally help-ful for all models.

Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of
Retrieval Augmentation to Language Models
Seiji Maekawa
Hayate Iso
Sairam Gurajada
Nikita Bhutani
Megagon Labs
{seiji,hayate,sairam,nikita}@megagon.ai
Abstract
While large language models (LMs) demon-
strate remarkable performance, they encounter
challenges in providing accurate responses
when queried for information beyond their pre-
trained memorization. Although augmenting
them with relevant external information can
mitigate these issues, failure to consider the ne-
cessity of retrieval may adversely affect overall
performance. Previous research has primarily
focused on examining how entities influence
retrieval models and knowledge recall in LMs,
leaving other aspects relatively unexplored. In
this work, our goal is to offer a more detailed,
fact-centric analysis by exploring the effects of
combinations of entities and relations. To facil-
itate this, we construct a new question answer-
ing (QA) dataset called WITQA (Wikipedia
Triple Question Answers).
This dataset in-
cludes questions about entities and relations
of various popularity levels, each accompanied
by a supporting passage. Our extensive exper-
iments with diverse LMs and retrievers reveal
when retrieval does not consistently enhance
LMs from the viewpoints of fact-centric pop-
ularity. Confirming earlier findings, we ob-
serve that larger LMs excel in recalling pop-
ular facts. However, they notably encounter
difficulty with infrequent entity-relation pairs
compared to retrievers. Interestingly, they can
effectively retain popular relations of less com-
mon entities. We demonstrate the efficacy of
our finer-grained metric and insights through
an adaptive retrieval system that selectively em-
ploys retrieval and recall based on the frequen-
cies of entities and relations in the question.1
1
Introduction
Large language models (LMs) (Brown et al., 2020;
OpenAI, 2023) have exhibited impressive capabil-
ities owing to their ability to retrieve knowledge
memorized during pre-training (Sanh et al., 2022;
1The code and data are available at https://github.com/
megagonlabs/witqa.
Wei et al., 2022; Ouyang et al., 2022). However,
despite the increase in model size and complexity,
LMs remain susceptible to factual inaccuracies in
knowledge-intensive tasks such as open domain
question answering and natural language genera-
tion, which demand access to a broader spectrum of
information (Petroni et al., 2021; Chen et al., 2017;
Lin et al., 2022). Retrieval-Augmented Language
Models (RALMs) (Guu et al., 2020; Lewis et al.,
2020; Izacard and Grave, 2021) have emerged as a
promising solution for mitigating factual errors by
incorporating relevant external information.
Nevertheless, recent studies suggest that RALMs
are not a universal solution (Petroni et al., 2020;
Li et al., 2023). Indiscriminately augmenting LMs
with irrelevant passages can override potentially
correct knowledge already possessed by the LM,
resulting in incorrect responses (illustrated in Table
1). A robust RALM is characterized by its ability
to accurately recall its prior knowledge while se-
lectively incorporating retrieved information only
when necessary.
Determining when to recall and when to retrieve
external information thus requires a thorough in-
vestigation of the following questions:
1. Under what conditions LMs can recall cor-
rectly and what factors influence their ability?
2. When retrieval augmented models make errors
and what factors affect their performance?
3. Are there any common error patterns between
LMs and retrieval models responses?
While previous research has investigated factors
influencing memorization in LMs as well as per-
formance of retrievers, they have a few limitations:
a) They solely focus on entities (Sun et al., 2023;
Mallen et al., 2023), whereas real-world informa-
tion comprises both entities and relations . b) They
primarily focus exclusively on either retrievers or
arXiv:2402.13492v3  [cs.CL]  27 Mar 2024
Triple: (Chicago, country, United States of America)
Entity Popularity: 95.0%ile
Question: What country is Chicago located in?
Entity-Relation Popularity: 97.4%ile
LM Answer: United States [Correct]
Context: The Chicago Municipal Tuberculosis Sanitarium was located in Chicago, Illinois, USA.. . . [Correct Retrieval]
RALM Answer: USA [Correct]
Triple: (George H.W. Bush, educated at, Yale University)
Entity Popularity: 89.5%ile
Question: What educational institution did George H.W. Bush attend?
Entity-Relation Popularity: 41.8%ile
LM Answer: Yale University [Correct]
Context: The George H.W. Bush Presidential Library is located on a site on the west campus of Texas A&M University in College Station, Texas.. .. [Wrong
Retrieval]
RALM Answer: Texas A&M University [Wrong]
Triple: (Ellen Litman, educated at, University of Pittsburgh)
Entity Popularity: 10.3%ile
Question: What educational institution was Ellen Litman educated at?
Entity-Relation Popularity: 17.9%ile
LM Answer: Stanford University [Wrong]
Context: Ellen Litman Ellen Litman (born 1973) is an American novelist. She received the Rona Jaffe Foundation Writers’ Award in 2006. Born in Moscow,
Russia, she emigrated with her parents in 1992 to Pittsburgh, Pennsylvania. She was educated at the University of Pittsburgh and earned a B.S. in Information
Science. .. . [Correct Retrieval]
RALM Answer: University of Pittsburgh [Correct]
Table 1: QA examples from WiTQA with predictions of varying popularity of question entity and entity-relation
pair. We show the predictions from LM (GPT-3.5) with no augmentation and RALM (GPT-3.5+BM25). In the top
row, both LM and RALM provide correct answers for the popular question. In the middle row, LM generates correct
answer but RALM provides incorrect answer due to retrieval errors. In the bottom row, LM provides incorrect
answer for an infrequent entity-relation pair.
recall in LMs, neglecting the interplay between
them (Petroni et al., 2019; Sciavolino et al., 2021;
Liu et al., 2023).
In this work, we aim to provide a finer-grained
(fact-centric) analysis by investigating the impact
of combinations of entities and relations on the
performance of RALMs. We focus on question
answering (QA) task, where we analyze 10 LMs of
varying sizes with 5 different retrieval settings. To
facilitate this, we require a QA benchmark that not
only provides valid supporting passages for each
QA pair but also integrates indicators for mem-
orization in LMs. Additionally, the benchmark
should encompass entities and relations of varying
popularity. However, existing benchmarks such
as PopQA (Mallen et al., 2023) and EntityQues-
tions (Sciavolino et al., 2021) are not suitable for
this purpose as they are entity-centric and target
long-tailed information. To facilitate fact-centric
analysis, we develop a novel dataset called WITQA
(Wikipedia Triple Question Answers). We sample
triples extracted from Wikipedia, considering the
popularity of entities and entity-relation combina-
tions. We then generate QA pairs for each triple,
ensuring that each example in WITQA is accompa-
nied by supporting passages and popularity scores
for the question entity-relation pair.
Our investigation of RALMs zero-shot per-
formance on the proposed benchmark dataset
(WITQA) yields the following key findings:
• Even without retrieval, LMs can correctly re-
call entity-relation pairs frequently encoun-
tered during pre-training. Nonetheless, this ca-
pability is notably contingent on the model’s
size. Larger models can acquire long-tailed
relations about renowned entities. However,
there is still a significant drop in overall per-
formance when addressing minor facts.
• For long-tailed entity-relation pairs, retrievers
show more robust performance compared to
recall abilities of LMs, suggesting that aug-
mentation for such cases is beneficial. How-
ever, this observation does not extend to well-
known entity-relation pairs, potentially lead-
ing to override issues.
• LMs achieve higher accuracy than retrievers
for well-known entity-relation pairs regarding
long-tailed entities while previous studies re-
port that large LMs struggle with long-tailed
entities.
Our findings reveal when retrieval does not as-
sist LMs through the lens of fact-level popular-
ity. Leveraging this insight, we propose a selective
memory integration, which selectively employs re-
trieval augmentation and LMs’ memory based on
the frequencies of entities and relations. Our exper-
iments demonstrate that this approach can enhance
QA performance by up to 10.1%.
2
Background
2.1
Open domain Question Answering
Open domain question answering is a knowledge-
intensive task that involves generating an answer
as an output a given a question q as an input. This
task typically involves retrieving relevant passage
p based on the given question q using a retrieval
2) Triple Sampling
Human validation
[Supporting passage]
…"Nausicaä of the Valley
of the Wind", was
serialized in "Animage"...
[Triple]
(Nausicaä of the Valley of the Wind, published in, Animage)
What Japanese anime and entertainment magazine was
"Nausicaä of the Valley of the Wind" published in?
If all the criteria are satisfied:
If any criterion is
NOT satisfied:
Send feedback, e.g.,
“the question cannot be answered…”
WiTQA
HasSubject
After k
iteration
NoObject
Answerable
Wikipedia
Wikidata
 1) Triple Extraction
S1, R1, O1
S1 is R1 …
S2, R2, O2
S3, R3, O3
・・・
…S2 plays …
…S3 … O3.
・・・
S-R counts
S counts
NLI
Passage
Triple
Sn, Rn, On
…Sn … On.
Passage
Triple
S1, R1, O1
S1 is R1 …
S3, R3, O3
・・・
…S3 … O3.
・・・
Sm, Rm, Om Sm attends Om…
S-R count S count
3
1500
53
5
1660
100
・・・
・・・
4) Question Generation with Roundtrip Refinement
3) Answer Candidate Expansion
Triple: (George H. W. Bush, educated at, Yale University)
Wikidata
Expanded Answers: {Yale University, Yale, Phillips Academy, …}
Bin-wise sampling
S-R count
# triples
Figure 1: Overview of WiTQA dataset creation. First, we extract triples from Wikipedia and Wikidata, and compute
the frequency of subject-relation pairs and subject entity (referred to as S-R counts and S counts) (§3.1.1). Second,
we sample triples based on different ranges of S-R counts and select supporting passages based on entailment
scores(§3.1.2). Third, we expand answer candidates using Wikidata (§3.1.3). Finally, we generate questions from
triples and iteratively refine generated questions (§3.1.4).
model Mret. Subsequently, the retrieved passage
is used to model the answer a using a reader model
Mread: Mread(a|q, p).
2.2
Parametric vs Non-Parametric Knowledge
Although, in the early study of the open domain
question answering, a reader model Mread always
relied on the external passage p to reliably an-
swer the question q (Chen et al., 2017; Radford
et al., 2019; Lee et al., 2019; Guu et al., 2020;
Lewis et al., 2020), recent LM-based reader mod-
els started showing the potential to answer the
question without relying on the external passage p:
Mread(a|q, p = ϕ) (Petroni et al., 2019; Roberts
et al., 2020; Brown et al., 2020; Mallen et al., 2023;
Yu et al., 2023a; Kandpal et al., 2023; Kang and
Choi, 2023; Shi et al., 2023). This has actually shed
light on the prospect that retrieval model Mret can
have a negative impact on RALMs. For example,
Li et al. (2023) showcased that if the irrelevant pas-
sage is provided to the LMs, the parametric knowl-
edge in LM is overridden by the non-parametric
knowledge in the passage p. In the context of open
domain question answering tasks, this suggests that
the LM-based reader model Mread may produce
incorrect answer ¯a because it can be misguided by
the irrelevant passage ¯p provided by the retrieval
model Mret.
To address this issue, various approaches have
been proposed (Yu et al., 2023b; Mallen et al.,
2023; Yoran et al., 2023; Asai et al., 2024). For
instance, Mallen et al. (2023) reported that combin-
ing LMs and RALMs based on the entity popularity
can yield improved QA accuracy. Asai et al. (2024)
built a training dataset to determine when to engage
in retrieval and to assess the relevance of retrieved
passages using GPT-4 and then fine-tuned smaller-
scale models to replicate similar behavior.
However, none of the above addresses the dis-
tinct strengths and weaknesses of LMs and retrieval
models, nor have they clarified the causes of errors
when these systems are interconnected. This pri-
marily stems from the lack of datasets designed
for such in-depth analysis. Thus, we introduce the
WITQA dataset, specifically created to analyze the
error patterns of LMs and retrieval models, both
independently and in tandem.
3
The WITQA dataset
The WITQA dataset is meticulously constructed
with the underlying assumption that LMs are likely
to recall facts frequently mentioned in their pre-
training corpus (Petroni et al., 2019; Jiang et al.,
2020). We curate question-answer pairs annotated
with the frequency of mentions of entities and
their relationships within Wikipedia—a predom-
inant source of pre-training for LMs—along with
their supporting passages. This enables us to ex-
plore the correlation between popularity in the pre-
training corpus and the performance of LMs and
retrieval models individually. Furthermore, when
operating in tandem, it facilitates the analysis of
whether RALM’s errors stem from LM’s reasoning
or primarily arise from retrieval errors.
3.1
Dataset creation
Our process for creating the dataset involves four
key steps: 1) extraction of triples from Wikipedia,
2) sampling of triples, 3) expansion of answer can-
didates, and 4) generation of questions with round-
trip refinement, as illustrated in Figure 1.
3.1.1
Triple extraction
We first extract triples from Wikipedia to estimate
the popularity of the subject entity (S count) and
the co-occurrence of the subject entity and rela-
Dataset
Page view
S count
S-R count
Supporting passages
# of Relation Type
Question form
EntityQuestions (Sciavolino et al., 2021)
✗
✗
✗
✗
24
Template
PopQA (Mallen et al., 2023)
✓
✗
✗
✗
16
Template
WiTQA (Ours)
✓
✓
✓
✓
32
Model-assisted
Table 2: Summary of question-answering datasets. WiTQA includes question popularity indicators and valid
supporting passages sourced from Wikipedia. It contains more diverse relation types. It employs a model-assisted
approach for question generation, leading to a more versatile verbalization of triples.
tion predicate (S-R count) in the Wikipedia cor-
pus.2
However, building a scalable and robust
information extraction system is a long-standing
challenge (Chia et al., 2022; Kim et al., 2023). To
address this, we opted to leverage the Wikipedia
hyperlink and Wikidata for rule-based triple ex-
traction (Elsahar et al., 2018; Huguet Cabot and
Navigli, 2021). Specifically, we extract a list of
entities from the Wikipedia abstract, map them to
Wikidata ID using Wikimapper,3 and finally extract
all the triples mentioned in the text by matching
them with the Wikidata database.
Given a list of extracted triples, we compute
S count for each unique subject entity in the list.
Similarly, we compute S-R count for each unique
subject entity-relation pair in the list. Additionally,
for each passage-triple pair, we compute entailment
score using NLI models.4 For a given triple, the
passage with the highest entailment score is des-
ignated as the supporting passage. Please refer to
Appendix A for more details.
3.1.2
Triple sampling
Concerning subject-relation (S-R) counts, the dis-
tribution of triples follows a long-tail pattern. To
ensure dataset diversity, we employed sampling
based on S-R counts. Specifically, we manually
sampled 32 relations and categorized triples into
intervals such as [1, 5), [5, 10), [10, 50), [50, 100),
[100, 500), [500, 1000), and 1000+. Subsequently,
we sampled up to 200 triples for each relation
within each interval.
3.1.3
Answer candidate expansion
Given that a question formulated with a subject S
and relation R can have multiple valid answers, it
is crucial to recognize that the object O in a sam-
pled triple (S,R,O) might not be the only correct
response to the question. To address this issue, we
2https://archive.org/download/enwiki-20211020/
enwiki-20211020-pages-articles-multistream.xml.
bz2
3https://github.com/jcklie/wikimapper
4https://huggingface.co/cross-encoder/
nli-deberta-v3-large
10
0
10
2
10
4
S-R counts
WiTQA
10
0
10
1
10
2
10
3
10
4
# questions
10
0
10
2
10
4
S-R counts
EntityQuestions
10
0
10
1
10
2
10
3
10
4
10
0
10
2
10
4
S-R counts
PopQA
10
0
10
1
10
2
10
3
10
4
Figure 2: Histograms of question distributions. WiTQA
exhibits greater diversity than existing benchmarks re-
garding question popularity, as indicated by the variation
in S-R counts.
define acceptable answers for a question derived
from a triple (S,R,O) as a set of entities E for which
(S,R,E) exists in Wikidata. These acceptable an-
swers include aliases of object entities listed in
Wikidata.
3.1.4
Question generation with roundtrip
refinement.
Previous studies employ a template-based method
to generate natural language questions for the triple-
passage pairs. This method involves crafting a
template for each relation with a placeholder for
the subject entity, and treating the object entities
as answers (Sciavolino et al., 2021; Mallen et al.,
2023). However, template-based approaches are
known to suffer from questions of poor quality
and diversity. For instance, consider the template
What sport does [SUBJ] play? for the relation
sport. This template works well for triples like
(Shohei Ohtani, sport, baseball), resulting
in a natural question: What sport does Shohei
Ohtani play?. However, it becomes problem-
atic with triples like (2008–09 Maltese Premier
League, sport, association football), lead-
ing to an awkward question: What sport does
the 2008–09 Maltese Premier League play?.
One potential solution is to have each question
written by a human; however, this approach is pro-
hibitively expensive. Therefore, we adopt a model-
assisted approach (Zhang et al., 2024) to automati-
cally generate questions for each triple, based on
Questions
14, 837
Unique subject entities
13, 251
Unique object entities
7, 642
Average length of supporting passages (characters)
214.3
Questions added in first roundtrip
12, 856
Questions added in second roundtrip
823
Questions added in third roundtrip
283
Questions written by annotators
743
Table 3: Statistics of WiTQA.
the round-trip question generation method (Alberti
et al., 2019). Specifically, we generate a question
given a context. We then use that generated ques-
tion and the context to generate the answer. To
assess the validity of a generated question, we es-
tablish three criteria:
• Answerable: marked as True if the model
accurately answers a generated question using
its passage.
• HasSubject: marked as True if a generated
question includes the subject entity of its
triple.
• NoObject: marked as True if a generated
question does not incorporate the object entity.
If a generated question fulfills all these criteria, it is
considered valid; otherwise, feedback is provided
to the model to enhance the question. We use GPT-
3.5 (Ouyang et al., 2022) for this step due to its
powerful language generation ability.
We observe that 95% of questions (14, 094
/14, 837) satisfy all the criteria after three round-
trip iterations. The remainder of the questions
(743/14, 837) were rewritten by our three internal
annotators. We divided the datasets into three over-
lapping sections and consulted with at least two
annotators to obtain human-written questions. We
used an annotation framework MEGAnno (Zhang
et al., 2022) for this annotation due to its flexible
labeling computational notebooks. Appendix B
shows all the prompts that we used for the question
generation and verification.
3.2
Dataset Statistics
By following the outlined data creation process,
the resulting dataset, WITQA, comprises a total
of 14, 837 questions. In order to position WITQA
within the landscape of factoid QA benchmarks, we
compare it with two established benchmarks: Enti-
tyQuestions (Sciavolino et al., 2021) and PopQA
(Mallen et al., 2023). Both of these benchmarks
generate their questions from Wikidata triples. Ta-
ble 2 illustrates that WITQA is unique among QA
benchmarks in that it includes supporting passages
and question popularity. The histograms in Fig-
ure 2 showcase the distribution of questions based
on the S-R counts. Thanks to our bin-wise triple
sampling, WITQA features a substantial number
of questions with over 1, 000 S-R counts, a char-
acteristic that EntityQuestions and PopQA seldom
possess. Additionally, we observe that 62% of
questions in EntityQuestions and 65% of questions
in PopQA have never appeared in triples extracted
from the Wikipedia abstract. In this context, ex-
isting QA datasets have less diversity in terms of
question popularity than WITQA. For a more in-
depth analysis of question popularity, we provide
subject entity counts and subject page views5 for
each question. We show the statistics of WITQA
in Table 3. Detailed statistics of WITQA are pre-
sented in the Appendix C.1.
4
Experiments: Recall or Retrieve
We evaluate 10 language models of varying sizes
augmented with four different retrieval methods
to quantify the recall capability of LMs and the
performance of retrievers in isolation and jointly
and share the insights.
4.1
Setup
Models. We use Flan-T5-small/base/large/xl (60M,
220M, 770M, and 3B) as small-scale LMs. We
consider instruction fine-tuned Mistral-7B (Jiang
et al., 2023) and Llama-2-7B/13B/70B-chat (Tou-
vron et al., 2023) for medium-scale LMs, and GPT-
3.5/GPT-4 for large-scale LMs(Ouyang et al., 2022;
OpenAI, 2023).6
Retrievers.
We consider seven retrievers that
include BM25 (Robertson et al., 2009), Con-
triever (Izacard et al., 2021), GTR-large, GTR-
xl (Ni et al., 2022), BGE7 (Xiao et al., 2023), Gen-
Read (Yu et al., 2023a) and Oracle. BM25 is a
static term-based sparse retriever that doesn’t re-
quire training. Contriever is a unsupervised dense
retriever pre-trained on a large corpora and fine-
tuned on MS-MARCO (Nguyen et al., 2016). GTR
and BGE are supervised dense retrievers pretrained
on a large corpora and then fine-tuned on various
5We obtain page views by querying the Wikipedia API.
6gpt-3.5-turbo-0613 and gpt-4-0613
7BAAI/bge-large-en
supervised datasets including NQ (Kwiatkowski
et al., 2019) and HotpotQA (Yang et al., 2018).
Both GTR and BGE retrieve from the Wikipedia
corpus that is also employed to create our bench-
mark. GenRead generates relevant passages by
prompting large language models. Oracle retriever
always retrieves the correct supporting passage for
a given QA pair. We include it to measure the rea-
soning capabilities of the models in isolation from
retriever errors. Please refer to Appendix C.3 for
more details.
Querying. We use the following two templates
for prompting a model.8 The first one (a) is gen-
erative prediction without any retrieval and (b) is
contextual generative prediction that uses retrieved
passage from one retrievers as a context.
Question:{{question}}
Answer:
(a) Generative
Prediction:
Context:{{context}}
Question:{{question}}
Answer:
(b) Contextual
Generative Prediction:
Metric. We mark a prediction as correct if any
sub-string of the prediction is an exact match of
any of the gold answers.
4.2
Analysis of Model’s Recall Ability
We explore the models’ capacity to recall factual
knowledge, considering models of various sizes
and questions with differing popularity of subject-
relation pairs. As depicted in Figure 3, all models,
regardless of their size, generally demonstrate good
recall of popular facts. For instance, even Flan-T5-
large can achieve up to 80% accuracy for questions
with over 1000 S-R count. Predictably, larger mod-
els exhibit a superior ability to recall compared to
smaller models. Remarkably, in the case of less
popular questions, there is a notable discrepancy
in accuracy between small models and medium-
/large-scale models. Some of these findings are
consistent with the insights from recent works on
evaluating factual knowledge in LMs (Sun et al.,
2023). Also, to compare S-R counts with subject
page view counts which are used in the existing
study (Mallen et al., 2023) as the question popu-
larity, we demonstrate that the accuracy of vanilla
LMs does not consistently increase with increasing
8Mallen et al. (2023) observed that more sophisticated
instructions don’t lead to significant improvements.
[1, 10)
[10, 100)
[100, 1000)
1000+
S-R counts
0.0
0.2
0.4
0.6
0.8
1.0
Accuracy
Flan-T5-small
Flan-T5-base
Flan-T5-large
Flan-T5-xl
Mistral-7B
Llama-2-7B-c
Llama-2-13B-c
Llama-2-70B-c
GPT-3.5
GPT-4
Figure 3: We categorize the questions into bins based on
their S-R counts and present LMs accuracy across these
bins. Shaded areas are the 95% bootstrap confidence in-
tervals with 1000 samples. Larger models exhibit higher
accuracy than smaller models. Even small models mem-
orize factual knowledge about popular questions.
[0, 10)
[10, 100)
[100, 1000)
1000+
Subject Page Views
0.0
0.2
0.4
0.6
0.8
1.0
Accuracy
Flan-T5-small
Flan-T5-base
Flan-T5-large
Flan-T5-xl
Mistral-7B
Llama-2-7B-c
Llama-2-13B-c
Llama-2-70B-c
GPT-3.5
GPT-4
Figure 4: Accuracy over subject entity page views.
page view count in Figure 4 while accuracy con-
sistently increases as S-R and S counts increase in
Figures 3 and 5, respectively. This suggests that our
proposed metrics are more robust than previously
proposed metrics.
4.3
When Do Retrievers Help
Next, we augment the models with the retrievers
and estimate the performances based on top-one
retrieved passage. While expanding the context
size could potentially enhance performance, we
leave the exploration of augmentation with top-k
passages for future investigations.
Figure 6 shows the results of various LMs with
and without augmentation. Generally, retrieval
augmentation enhances model performance, par-
ticularly for smaller models. However, the perfor-
mance gap tends to be smaller, and sometimes even
negative, for larger models like Llama-2 and the
GPT series. GPT models often generate responses
like “The context does not provide information
on...” when the retrieved passages are insufficient
to answer the question.We observe a similar pattern
[0, 10)
[10, 100)
[100, 1000)
1000+
S counts
0.0
0.2
0.4
0.6
0.8
1.0
Accuracy
Flan-T5-small
Flan-T5-base
Flan-T5-large
Flan-T5-xl
Mistral-7B
Llama-2-7B-c
Llama-2-13B-c
Llama-2-70B-c
GPT-3.5
GPT-4
Figure 5: Accuracy over entity counts.
in Llama models, although it occurs less frequently.
In essence, retrieved information often supersedes
recall in larger models, suggesting that these mod-
els have high recall but are also more susceptible
to retrieval errors.
Our observations indicate that BM25, Contriever,
GTR, and BGE notably enhance the accuracy of
small and medium models by up to 49.2%, while
GenRead appears to be more effective for larger
models with up to 4.1% improvement. This sug-
gests that the ability to maintain a coherent chain-
of-thought emerges more prominently when the
model size is sufficiently large.
We observe a significant improvement in ac-
curacy for all models, irrespective of their sizes,
when augmented with Oracle. Even the small-
est model, Flan-T5-small, achieves an accuracy
of 78.1% with Oracle passages. However, sub-
stantial differences are observed between perfor-
mances with BM25/Contriever/GTR/BGE and Or-
acle, which can be attributed to retrieval errors.
When augmented with incorrect passages, most
models struggle to provide correct answers.
Finally, we discuss why the small models aug-
mented with Oracle passages obtain lower accuracy
than larger models. We observe that when the con-
text has multiple entities, the small models tend
to predict the entity that appears close to the ques-
tion words, which may not be correct. We give an
example:
Context:
Susanna Wesley (née Annesley; 20 January
1669 – 23 July 1742) was the daughter of
Dr Samuel Annesley and Mary White, and
the mother of John and Charles Wesley
Question:
Who was the mother of Charles Wesley?
For example, Flan-T5-small answers “Mary
White” while the true answer is “Susanna Wes-
ley”. Note that the context supports the true an-
swer, but Flan-T5-small cannot answer correctly.
Highlighted by the example, we observe that small
models tend to extract an entity as an answer, which
appears near from the words in questions, leading
to lower accuracy compared to larger models.
4.4
Deep Dive into Errors
In order to closely examine the factors influencing
recall and retrieval, we concentrate on GPT-3.5 as
the baseline model, along with the four retrievers.
How does popularity affect RALM perfor-
mance?
Figure 7 shows the accuracy of GPT-3.5
on questions with different subject-relation (S-R)
counts. Notably, the accuracy of Vanilla and Gen-
Read is considerable for popular questions but sig-
nificantly declines when S-R counts drop below
10. This drop is likely due to models memoriz-
ing popular facts. Conversely, RALMs utilizing
BM25, Contriever, GTR, and BGE exhibit greater
robustness on less popular questions. However,
for popular questions, their performance is inferior
compared to the Vanilla model without augmenta-
tion. Given that supporting passages (Oracle) con-
sistently enhance the performance of models, we
hypothesize that retrieval errors for popular ques-
tions negatively impact the performance of RALMs
using BM25, Contriever, GTR, and BGE.
Is there a correlation between RALM perfor-
mance and retrieval errors?
To verify the above
hypothesis, we closely examine the relationship
between RALM performance and retrieval errors
for questions of varying popularity. To estimate
retriever performance, we compute passage ac-
curacy, marking a passage as correct if any sub-
string of the passage is an exact match for a gold
answer. We report the passage accuracy of dif-
ferent retrieval models in Figure 8. This trend
aligns closely with RALM performance, as illus-
trated in Figure 7. Specifically, agreement ratios
between RALM performance and passage accu-
racy are 85.5%, 82.4%, 88.5%, 88.4%, 88.7%, and
93.1% for BM25, Contriever, GTR-large, GTR-xl,
BGE, and Oracle, respectively. Upon closer exami-
nation, we find that although retriever accuracy is
low for rare questions, the drop is less significant
compared to Vanilla LMs (see the leftmost plots in
Figures 7 and 8). This indicates that retrieval aug-
mentation is still beneficial for rare facts compared
Flan-T5-small Flan-T5-base Flan-T5-large
Flan-T5-xl
Mistral-7B
Llama-2-7B
Llama-2-13B Llama-2-70B
GPT-3.5
GPT-4
0.0
0.2
0.4
0.6
0.8
Accuracy
Vanilla
GenRead
BM25
Contriever
GTR-large
GTR-xl
BGE
Oracle
Figure 6: Overall accuracy of Vanilla LMs and LMs augmented with GenRead, BM25, Contriever, GTR-large,
GTR-xl, BGE, and supporting passages (Oracle). Error bars show the 95% bootstrap confidence intervals with
1000 samples. Accuracy of Vanilla and GenRead improves with larger model sizes. Augmentation with retrievers
enhances accuracy, especially for smaller models. However, the gap diminishes and, in some cases, becomes
negative for larger models such as the GPT series. Supporting passages (Oracle) prove beneficial for all models.
[1, 10)
[10, 100)
[100, 1000)
1000+
S-R counts
0.2
0.4
0.6
0.8
1.0
Accuracy
Vanilla
GenRead
BM25
Contriever
GTR-large
GTR-xl
BGE
Oracle
Figure 7: Accuracy of different retrievers with GPT-3.5
across varying question popularity.
[1, 10)
[10, 100)
[100, 1000)
1000+
S-R Counts
0.0
0.2
0.4
0.6
0.8
1.0
Passage Accuracy
BM25
Contriever
GTR-large
GTR-xl
BGE
Oracle
Figure 8: Passage accuracy across question popularity.
to recall. In contrast, Vanilla LMs perform better
than retrievers for popular questions.
How does model size affect the need for augmen-
tation?
We further explore how the size of the
model influences the need for augmentation based
on the popularity of questions, considering that the
ability to recall varies with model size. To this end,
we categorize the questions into four groups based
on the median of S-R counts (5) and S counts (12):
• head-head: both S-R and S counts are high
• head-tail: S-R count is high but S count is low
• tail-head: S-R count is low and S count is high
0.0
0.2
0.4
0.6
0.8
1.0
Accuracy
head-tail (n=1548)
0.0
0.2
0.4
0.6
0.8
1.0
head-head (n=6889)
0.0
0.2
0.4
0.6
0.8
1.0
Accuracy
tail-tail (n=5682)
0.0
0.2
0.4
0.6
0.8
1.0
tail-head (n=718)
S counts
S-R counts
Flan-T5-small
Flan-T5-base
Flan-T5-large
Flan-T5-xl
Mistral-7B
Llama-2-7B
Llama-2-13B
Llama-2-70B
GPT-3.5
GPT-4
Passage Accuracy
BM25
Contriever
GTR-large
GTR-xl
BGE
Figure 9: Analysis on Vanilla LMs with BM25, Con-
triver, GTR, and BGE passage accuracy over S-R counts
and S counts (n = the number of questions in the group).
In the top row, S-R counts are higher than the median.
In the bottom row, they are less than or equal to the
median. In the left column, S counts are less than or
equal to the median and in the right column, they are
higher than the median.
• tail-tail: both counts are low
Figure 9 shows the accuracy of various models
along with BM25, Contriver, GTR, and BGE pas-
sage accuracy across the four groups. Note that
we plot QA accuracy and passage accuracy since
we use the same criteria for them. We observe that
supervised retrievers, GTR and BGE, outperform
vanilla LMs in all groups. This is attributed to the
fact that it was fine-tuned with QA datasets about
Wikipedia. For the generalizability to broader do-
mains, we discuss the results with an unsupervised
retriever BM25 in the following.9
In the head-head group, medium and large mod-
els exhibit superior performance even without aug-
mentation, e.g., the GPT series achieve higher than
9Similar results were observed for Contriever; hence, we
focus on BM25 for simplicity.
80% accuracy. In contrast, there is a notable gap
between the accuracy of the vanilla model and pas-
sage accuracy for small models. This suggests that
only small models need augmentation for questions
in this group.
In the head-tail and tail-head groups, Llama and
GPT series achieve comparable or superior accu-
racy to the passage accuracy. This indicates that
large models can memorize long-tailed relations
of popular entities and popular relations of infre-
quent entities. On the other hand, medium-scale
models fail to recall compared to passage accu-
racy, and hence, they will benefit from augmenta-
tion. Interestingly, the passage accuracy in the tail-
head group is significantly lower (less than 60%)
compared to other groups. This retrieval error is
caused by frequent mentions of the entity but in-
frequent mentions of subject-relation pairs. Con-
sequently, accurately identifying relevant passages
from a large pool of passages containing the ques-
tion entity becomes challenging.
In the tail-tail group, even GPT series struggle to
recall long-tail information for rare entities. Since
retrievers are robust for such information, we con-
clude that retrieval augmentation is generally help-
ful for all models.
Does a combination of recall and retrieval im-
prove accuracy?
Based on these observations,
we hypothesize that an optimal combination of re-
call and retrieval can be obtained using S-R and
S counts as thresholds. We develop and examine
a selective memory integration method that uses
augmentation based on S-R and S counts, while
using vanilla LM as default. This method yielded
an improvement of 10.1% and 8.1% over Vanilla
and BM25 for GPT-3.5 and GPT-4, respectively.
For more details on this investigation, refer to the
Appendix C.2.
5
Further Related Work
Several recent studies (Asai et al., 2024; Yu et al.,
2023b; Ma et al., 2023; Zhang et al., 2023) have
focused on enhancing the robustness of RALMs by
assessing the usefulness and relevance of retrieved
passages to questions.
Yu et al. (2023b) has proposed the Chain-of-
Note framework, which systematically evaluates
the relevance and accuracy of retrieved passages,
thereby improving the noise robustness of RALMs.
Ma et al. (2023) has addressed query rewriting to
improve retriever performance by using LMs or
pre-trained LMs that are fine-tuned for a suitable
query rewriter. Zhang et al. (2023) has introduced
an approach that leverages two sources of infor-
mation: retrieved passages and LM-generated pas-
sages. This approach is designed on the hypothesis
that answers corroborated by both sources have a
higher likelihood of being accurate.
These studies rely on model-centric approaches
for assessing document relevance to queries, refin-
ing queries, and integrating retrieved and generated
passages. Thus, they overlook the importance of
data-centric question popularity as an indicator for
deciding when to retrieve and augment information.
In contrast, our study leverages question popularity
metrics derived from Wikipedia, offering insights
that are complementary and distinct from these ex-
isting methodologies.
6
Conclusion
We introduced WITQA, a novel QA dataset com-
prising of QA pairs associated with supporting pas-
sages. This dataset enables us to assess the perfor-
mance of LMs with respect to question popularity
and retrieval-augmentation. We conducted exten-
sive experiments investigating the zero-shot perfor-
mance of 10 LMs with four different retrieval meth-
ods. Our findings reveal several key insights: 1) the
ability to recall factual knowledge is influenced by
the model’s size, with even the GPT series facing
challenges when addressing minor facts; 2) small
RALMs demonstrate robust QA performance when
provided with supporting passages, suggesting that
errors in RALMs are primarily due to retrieval er-
rors; and 3) retrievers exhibit greater robustness for
long-tail information of long-tail entities compared
to the recall capability of LMs.
Limitations
Distribution of pre-training corpus.
This work
hypothesizes that the distribution of the Wikipedia
texts reflect that of pre-trained texts. However, the
pre-trained texts of recent proprietary models such
as GPT-4 are not accessible.
Prompt engineering.
A limited amount of
prompt tuning was conducted. For example, larger
models tend to refrain from generating answers
when the provided passage does not pertain to the
question. From the viewpoint of maximizing QA
accuracy, encouraging models to actively formu-
late answers regardless of passage relevance seems
advantageous. Yet, this approach could elevate
the proportion of incorrect answers, which is un-
desirable in practical applications. Exploring this
trade-off will be a focus of our future work.
Multi-hop relations.
Real-world questions of-
ten exhibit a complexity beyond simple triple-based
questions, for instance, encompassing multi-hop
relations. In this case, determining the subject and
relation can be challenging. However, to conduct
a deep analysis of LMs with clearly characterized
questions, we focus on triple-based questions in
this work.
Ethical Consideration
We use internal annotators for question rewriting
in Section 3.1.4, who were explained how data will
be used by the authors directly and earned more
than the minimal wage.
Acknowledgment
We thank Dan Zhang and Kushan Mitra for their
help with annotation during the data creation pro-
cess and Tanmay Laud and Estevam Hruschka for
valuable discussion.
References
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin,
and Michael Collins. 2019. Synthetic QA corpora
generation with roundtrip consistency. In Proceed-
ings of the 57th Annual Meeting of the Association for
Computational Linguistics, pages 6168–6173, Flo-
rence, Italy. Association for Computational Linguis-
tics.
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and
Hannaneh Hajishirzi. 2024. Self-RAG: Learning to
retrieve, generate, and critique through self-reflection.
In The Twelfth International Conference on Learning
Representations.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie
Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind
Neelakantan, Pranav Shyam, Girish Sastry, Amanda
Askell, Sandhini Agarwal, Ariel Herbert-Voss,
Gretchen Krueger, Tom Henighan, Rewon Child,
Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens
Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma-
teusz Litwin, Scott Gray, Benjamin Chess, Jack
Clark, Christopher Berner, Sam McCandlish, Alec
Radford, Ilya Sutskever, and Dario Amodei. 2020.
Language models are few-shot learners.
In Ad-
vances in Neural Information Processing Systems,
volume 33, pages 1877–1901. Curran Associates,
Inc.
Danqi Chen, Adam Fisch, Jason Weston, and Antoine
Bordes. 2017. Reading Wikipedia to answer open-
domain questions. In Proceedings of the 55th Annual
Meeting of the Association for Computational Lin-
guistics (Volume 1: Long Papers), pages 1870–1879,
Vancouver, Canada. Association for Computational
Linguistics.
Yew Ken Chia, Lidong Bing, Soujanya Poria, and Luo
Si. 2022. RelationPrompt: Leveraging prompts to
generate synthetic data for zero-shot relation triplet
extraction. In Findings of the Association for Compu-
tational Linguistics: ACL 2022, pages 45–57, Dublin,
Ireland. Association for Computational Linguistics.
Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci,
Christophe Gravier, Jonathon Hare, Frederique Lafor-
est, and Elena Simperl. 2018. T-REx: A large scale
alignment of natural language with knowledge base
triples. In Proceedings of the Eleventh International
Conference on Language Resources and Evaluation
(LREC 2018), Miyazaki, Japan. European Language
Resources Association (ELRA).
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat,
and Mingwei Chang. 2020. Retrieval augmented
language model pre-training. In Proceedings of the
37th International Conference on Machine Learning,
volume 119 of Proceedings of Machine Learning
Research, pages 3929–3938. PMLR.
Pere-Lluís Huguet Cabot and Roberto Navigli. 2021.
REBEL: Relation extraction by end-to-end language
generation. In Findings of the Association for Com-
putational Linguistics: EMNLP 2021, pages 2370–
2381, Punta Cana, Dominican Republic. Association
for Computational Linguistics.
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Se-
bastian Riedel, Piotr Bojanowski, Armand Joulin,
and Edouard Grave. 2021. Unsupervised dense infor-
mation retrieval with contrastive learning.
Gautier Izacard and Edouard Grave. 2021. Leveraging
passage retrieval with generative models for open do-
main question answering. In Proceedings of the 16th
Conference of the European Chapter of the Associ-
ation for Computational Linguistics: Main Volume,
pages 874–880, Online. Association for Computa-
tional Linguistics.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Men-
sch, Chris Bamford, Devendra Singh Chaplot, Diego
de las Casas, Florian Bressand, Gianna Lengyel, Guil-
laume Lample, Lucile Saulnier, Lélio Renard Lavaud,
Marie-Anne Lachaux, Pierre Stock, Teven Le Scao,
Thibaut Lavril, Thomas Wang, Timothée Lacroix,
and William El Sayed. 2023. Mistral 7b.
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham
Neubig. 2020. How can we know what language
models know? Transactions of the Association for
Computational Linguistics, 8:423–438.
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric
Wallace, and Colin Raffel. 2023. Large language
models struggle to learn long-tail knowledge. In
Proceedings of the 40th International Conference
on Machine Learning, volume 202 of Proceedings
of Machine Learning Research, pages 15696–15707.
PMLR.
Cheongwoong Kang and Jaesik Choi. 2023. Impact
of co-occurrence on factual knowledge of large lan-
guage models. In Findings of the Association for
Computational Linguistics: EMNLP 2023, pages
7721–7735, Singapore. Association for Computa-
tional Linguistics.
Bosung Kim, Hayate Iso, Nikita Bhutani, Estevam Hr-
uschka, Ndapa Nakashole, and Tom Mitchell. 2023.
Zero-shot triplet extraction by template infilling. In
Proceedings of the 13th International Joint Confer-
ence on Natural Language Processing and the 3rd
Conference of the Asia-Pacific Chapter of the Associ-
ation for Computational Linguistics (Volume 1: Long
Papers), pages 272–284, Nusa Dua, Bali. Association
for Computational Linguistics.
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Red-
field, Michael Collins, Ankur Parikh, Chris Alberti,
Danielle Epstein, Illia Polosukhin, Jacob Devlin, Ken-
ton Lee, Kristina Toutanova, Llion Jones, Matthew
Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob
Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natu-
ral questions: A benchmark for question answering
research. Transactions of the Association for Compu-
tational Linguistics, 7:452–466.
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova.
2019. Latent retrieval for weakly supervised open
domain question answering. In Proceedings of the
57th Annual Meeting of the Association for Computa-
tional Linguistics, pages 6086–6096, Florence, Italy.
Association for Computational Linguistics.
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio
Petroni, Vladimir Karpukhin, Naman Goyal, Hein-
rich Küttler, Mike Lewis, Wen-tau Yih, Tim Rock-
täschel, et al. 2020. Retrieval-augmented generation
for knowledge-intensive nlp tasks. Advances in Neu-
ral Information Processing Systems, 33:9459–9474.
Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin
Wang, Michal Lukasik, Andreas Veit, Felix Yu, and
Sanjiv Kumar. 2023. Large language models with
controllable working memory. In Findings of the As-
sociation for Computational Linguistics: ACL 2023,
pages 1774–1793, Toronto, Canada. Association for
Computational Linguistics.
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang,
Xingyu Dang, and Song Han. 2023. Awq: Activation-
aware weight quantization for llm compression and
acceleration. arXiv.
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022.
TruthfulQA: Measuring how models mimic human
falsehoods. In Proceedings of the 60th Annual Meet-
ing of the Association for Computational Linguistics
(Volume 1: Long Papers), pages 3214–3252, Dublin,
Ireland. Association for Computational Linguistics.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang,
Hiroaki Hayashi, and Graham Neubig. 2023. Pre-
train, prompt, and predict: A systematic survey of
prompting methods in natural language processing.
ACM Computing Surveys, 55(9):1–35.
Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao,
and Nan Duan. 2023. Query rewriting for retrieval-
augmented large language models. In Proceedings
of the 2023 Conference on Empirical Methods in
Natural Language Processing (EMNLP).
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das,
Daniel Khashabi, and Hannaneh Hajishirzi. 2023.
When not to trust language models: Investigating
effectiveness of parametric and non-parametric mem-
ories. In Proceedings of the 61st Annual Meeting of
the Association for Computational Linguistics (Vol-
ume 1: Long Papers), pages 9802–9822, Toronto,
Canada. Association for Computational Linguistics.
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao,
Saurabh Tiwary, Rangan Majumder, and Li Deng.
2016.
Ms marco: A human generated machine
reading comprehension dataset.
arXiv preprint
arXiv:1611.09268.
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo
Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan,
Keith Hall, Ming-Wei Chang, and Yinfei Yang. 2022.
Large dual encoders are generalizable retrievers. In
Proceedings of the 2022 Conference on Empirical
Methods in Natural Language Processing, pages
9844–9855, Abu Dhabi, United Arab Emirates. As-
sociation for Computational Linguistics.
OpenAI. 2023. Gpt-4 technical report. arXiv preprint
arXiv:2303.08774.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida,
Carroll Wainwright, Pamela Mishkin, Chong Zhang,
Sandhini Agarwal, Katarina Slama, Alex Gray, John
Schulman, Jacob Hilton, Fraser Kelton, Luke Miller,
Maddie Simens, Amanda Askell, Peter Welinder,
Paul Christiano, Jan Leike, and Ryan Lowe. 2022.
Training language models to follow instructions with
human feedback. In Advances in Neural Information
Processing Systems.
Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim
Rocktäschel, Yuxiang Wu, Alexander H. Miller, and
Sebastian Riedel. 2020. How context affects lan-
guage models’ factual predictions. In Automated
Knowledge Base Construction.
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick
Lewis, Majid Yazdani, Nicola De Cao, James Thorne,
Yacine Jernite, Vladimir Karpukhin, Jean Maillard,
Vassilis Plachouras, Tim Rocktäschel, and Sebastian
Riedel. 2021. KILT: a benchmark for knowledge
intensive language tasks. In Proceedings of the 2021
Conference of the North American Chapter of the
Association for Computational Linguistics: Human
Language Technologies, pages 2523–2544, Online.
Association for Computational Linguistics.
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel,
Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and
Alexander Miller. 2019. Language models as knowl-
edge bases?
In Proceedings of the 2019 Confer-
ence on Empirical Methods in Natural Language Pro-
cessing and the 9th International Joint Conference
on Natural Language Processing (EMNLP-IJCNLP),
pages 2463–2473, Hong Kong, China. Association
for Computational Linguistics.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan,
Dario Amodei, Ilya Sutskever, et al. 2019. Language
models are unsupervised multitask learners. Techni-
cal report, OpenAI.
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020.
How much knowledge can you pack into the param-
eters of a language model? In Proceedings of the
2020 Conference on Empirical Methods in Natural
Language Processing (EMNLP), pages 5418–5426,
Online. Association for Computational Linguistics.
Stephen Robertson, Hugo Zaragoza, et al. 2009. The
probabilistic relevance framework: Bm25 and be-
yond. Foundations and Trends® in Information Re-
trieval, 3(4):333–389.
Victor Sanh, Albert Webson, Colin Raffel, Stephen
Bach, Lintang Sutawika, Zaid Alyafeai, Antoine
Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey,
M Saiful Bari, Canwen Xu, Urmish Thakker,
Shanya Sharma Sharma, Eliza Szczechla, Taewoon
Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti
Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han
Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong,
Harshit Pandey, Rachel Bawden, Thomas Wang, Tr-
ishala Neeraj, Jos Rozen, Abheesht Sharma, An-
drea Santilli, Thibault Fevry, Jason Alan Fries, Ryan
Teehan, Teven Le Scao, Stella Biderman, Leo Gao,
Thomas Wolf, and Alexander M Rush. 2022. Multi-
task prompted training enables zero-shot task gener-
alization. In International Conference on Learning
Representations.
Christopher Sciavolino, Zexuan Zhong, Jinhyuk Lee,
and Danqi Chen. 2021. Simple entity-centric ques-
tions challenge dense retrievers. In Proceedings of
the 2021 Conference on Empirical Methods in Natu-
ral Language Processing, pages 6138–6148, Online
and Punta Cana, Dominican Republic. Association
for Computational Linguistics.
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan
Scales, David Dohan, Ed Chi, Nathanael Schärli, and
Denny Zhou. 2023. Large language models can be
easily distracted by irrelevant context. In Proceed-
ings of the 40th International Conference on Machine
Learning, ICML’23. JMLR.org.
Kai Sun, Yifan Ethan Xu, Hanwen Zha, Yue Liu, and
Xin Luna Dong. 2023. Head-to-tail: How knowl-
edgeable are large language models (llm)? a.k.a. will
llms replace knowledge graphs?
Hugo Touvron, Louis Martin, Kevin Stone, Peter Al-
bert, Amjad Almahairi, Yasmine Babaei, Nikolay
Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti
Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton
Ferrer, Moya Chen, Guillem Cucurull, David Esiobu,
Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,
Cynthia Gao, Vedanuj Goswami, Naman Goyal, An-
thony Hartshorn, Saghar Hosseini, Rui Hou, Hakan
Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa,
Isabel Kloumann, Artem Korenev, Punit Singh Koura,
Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Di-
ana Liskovich, Yinghai Lu, Yuning Mao, Xavier Mar-
tinet, Todor Mihaylov, Pushkar Mishra, Igor Moly-
bog, Yixin Nie, Andrew Poulton, Jeremy Reizen-
stein, Rashi Rungta, Kalyan Saladi, Alan Schelten,
Ruan Silva, Eric Michael Smith, Ranjan Subrama-
nian, Xiaoqing Ellen Tan, Binh Tang, Ross Tay-
lor, Adina Williams, Jian Xiang Kuan, Puxin Xu,
Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan,
Melanie Kambadur, Sharan Narang, Aurelien Ro-
driguez, Robert Stojnic, Sergey Edunov, and Thomas
Scialom. 2023. Llama 2: Open foundation and fine-
tuned chat models.
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu,
Adams Wei Yu, Brian Lester, Nan Du, Andrew M.
Dai, and Quoc V Le. 2022. Finetuned language mod-
els are zero-shot learners. In International Confer-
ence on Learning Representations.
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas
Muennighof. 2023. C-pack: Packaged resources to
advance general chinese embedding. arXiv preprint
arXiv:2309.07597.
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio,
William Cohen, Ruslan Salakhutdinov, and Christo-
pher D. Manning. 2018. HotpotQA: A dataset for
diverse, explainable multi-hop question answering.
In Proceedings of the 2018 Conference on Empiri-
cal Methods in Natural Language Processing, pages
2369–2380, Brussels, Belgium. Association for Com-
putational Linguistics.
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan
Berant. 2023. Making retrieval-augmented language
models robust to irrelevant context.
Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu,
Mingxuan Ju, Soumya Sanyal, Chenguang Zhu,
Michael Zeng, and Meng Jiang. 2023a. Generate
rather than retrieve: Large language models are
strong context generators.
The Eleventh Interna-
tional Conference on Learning Representations.
Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin
Ma, Hongwei Wang, and Dong Yu. 2023b. Chain-of-
note: Enhancing robustness in retrieval-augmented
language models. arXiv preprint arXiv:2311.09210.
Dan Zhang, Hannah Kim, Rafael Li Chen, Eser Kan-
dogan, and Estevam Hruschka. 2022. MEGAnno:
Exploratory labeling for NLP in computational note-
books. In Proceedings of the Fourth Workshop on
Data Science with Human-in-the-Loop (Language
Advances), pages 1–7, Abu Dhabi, United Arab Emi-
rates (Hybrid). Association for Computational Lin-
guistics.
Haopeng Zhang, Hayate Iso, Sairam Gurajada, and
Nikita Bhutani. 2024.
XATU: A Fine-grained
Instruction-based Benchmark for Explainable Text
Updates. In The 2024 Joint International Conference
On Computational Linguistics, Language Resources
and Evaluation.
Yunxiang Zhang, Muhammad Khalifa, Lajanugen Lo-
geswaran, Moontae Lee, Honglak Lee, and Lu Wang.
2023. Merging generated and retrieved knowledge
for open-domain QA. In The 2023 Conference on
Empirical Methods in Natural Language Processing.
A
Detailed Setup for NLI Scores of
Supporting Passages
As discussed in Section 3.1, we use the entailment
prediction to choose the best supporting passage
for each triple. For each triple, we input the text
containing both entities from the Wikipedia ab-
stract, and the triple in their surface forms, subject
+ relation + object, separated by the <sep> token.
Then, we simply select the passage with the highest
NLI score for each unique triple and use it as the
supporting passage of the triple.
B
Prompts for Question
Generation/Refinement and
Experiments
First, we provide a prompt used for our question
generation in Figure 10. Next, we show prompts
used for QA tasks in Figures 11 and 12. Also,
Figure 13 shows a prompt template to generate
related passages by LMs for GenRead, which was
introduced in its original paper.
Also, Algorithm 1 describes the roundtrip refine-
ment. In line 1, we initialize Message as Question
generated by a prompt in Figure 10. In line 3, we
obtain an answer to the question with its supporting
passage by using a prompt in Figure 12. Then, we
check the three criteria “Answerable”, “HasSub-
ject”, and “NoObject” for the question and answer
pair. If the pair satisfies all the criteria, the ques-
tion is added to WiTQA in line 6. Otherwise we
append a message that indicates which criteria are
not satisfied in lines 8–27. In lines 17–20, the al-
gorithm describe an exceptional case in which the
object of a triple is a substring of the subject and
questions are answerable. Since questions cannot
satisfy the criteria “HasSubject” and “NoObject”
in this case, we use this condition to add such ques-
tions to WiTQA. Before proceeding to the next
iteration, we regenerate a question with the mes-
sage by using GPT-3.5 in line 28. If we cannot
Given a context and a triple (subject,
relation, object), transform the triple to a
question that asks “Object”. The generated
question must contain a given “Subject”
and also be answerable without the context.
# Context:
{{ context }}
# Triple:
## Subject:
{{ subject }}
## Relation:
{{ Relation }}
(Meaning: {{ relation_description }})
## Object:
{{ object }}
# Output:
<question only and must
contain Subject>
Figure 10: A prompt for question generation. “{{ con-
text }}”, “{{ subject }}”, “{{ relation }}”, and “{{
object }}” are replaced with the supporting passage,
subject, relation, and object of a given triple. “{{ re-
lation_description }}” is a description of the relation,
which is provided in Wikidata.
obtain a question-answer pair satisfying the three
criteria through k iterations, the authors write the
questions based on the triples.
C
Additional Experimental Details
Implementation.
For open LMs, we execute
all experiments on a GPU node with 8 NVIDIA
A100-SXM cores. As for Llama-2-70B, we use
AWQ quantization (Lin et al., 2023) to make it fit
our GPU. We set the temperature parameter to 0
for all experiments.
C.1
Additional WiTQA Statistics
Table 4 shows 32 relations used in WiTQA and the
number of questions containing each relation in
WiTQA.
In Table 5, we show relation counts indicating
how many times each relation appears in all ex-
tracted triples in Wikipedia abstract. Thanks to the
bin-wise triple sampling described in Section 3.1,
Algorithm 1 Roundtrip question refinement
Input: Question, k
Output: Message
1: Message ←[Question]
▷Use a question generation prompt template in Figure 10
2: for i in range(k)
3:
Answer ←GPT-3.5(Message)
▷Use a RAG prompt template in Figure 12
4:
Answerable, HasSubject, NoObject ←Check_criteria(Question, Answer)
5:
if Answerable & HasSubject & NoObject
6:
Add Question to WiTQA
7:
break
8:
else if !Answerable & HasSubject & NoObject
9:
Message.append(“It is good that the question contains ‘Subject’ and not ‘Object’, but the
question cannot be answered. Make the question more detailed if needed. Try again.”)
10:
else if !HasSubject & NoObject
11:
Message.append(“The question you generated does not contain ‘Subject’, but ‘Subject’ must
be in the question. Try again.”)
12:
else if !HasSubject & !NoObject
13:
Message.append(“The question you generated does not contain ‘Subject’ and does contain
‘Object’. However, ‘Subject’ must be in the question. Also, ‘Object’ must not be in the
question. Try again.”)
14:
else if HasSubject & !NoObject
15:
if Question.subject in Question.object & Question.subject ! = Question.object
16:
Message.append(“The question you generated contains ‘Subject’ and ‘Object’, but
‘Object’ must not be in the question. Though ‘Subject’ is the substring of ‘Object’,
remove ‘Object’ and remain only ‘Subject’.”)
17:
else if Question.object in Question.subject
18:
if Answerable
19:
Add Question to WiTQA
20:
break
21:
else
22:
Message.append(“It is good that the question contains ‘Subject’, but the question
cannot be answered. Make the question more detailed if needed. Try again.”)
23:
end if
24:
else
25:
Message.append(“The question you generated contains ‘Subject’ and ‘Object’, but
‘Object’ must not be in the question. Remove ‘Object’ and remain only ‘Subject’.”)
26:
end if
27:
end if
28:
Message.append(GPT-3.5(Message))
▷Concatenate a refined question into Message
29: end for
# Question:
{{ question }}
# Answer: <answer only>
Figure 11: A prompt template for vanilla. “{{ question
}}” is replaced with an actual question.
WiTQA successfully captures triples with long-tail
relations such as “cuisine” and “medical condition”,
improving the diversity of its questions.
C.2
Selective Memory Integration
In Section 4.4, our analysis revealed that while
RALMs exhibit enhanced performance over
Vanilla LMs for less popular questions, Vanilla
LMs achieve superior performance in handling
more popular questions. This finding suggests a
complementary relationship between Vanilla LMs
and RALMs, contingent upon the popularity of
the questions. The above observation motivates us
to selectively integrate LMs and RALMs with the
larger sizes to improve overall accuracy by using
the question popularity as an indicator to decide
when to augment or not. As shown in Figure 9,
if both S-R and S counts are small (the tail-tail
group), we need to augment LMs with retrieved
passages; otherwise Vanilla and GenRead obtain
higher accuracy. Hence, we take an approach that
uses an RALM for the tail-tail group and Vanilla
LM for other groups.
C.2.1
Settings
We estimate optimal thresholds of S-R counts and S
counts for each relation by using 50% of questions
in WiTQA, i.e., we find optimal thresholds for S-R
and S counts so that the thresholds maximize the
overall accuracy. Concretely, we use Vanilla LMs
Given a context and a question, answer the
question.
# Context:
{{ context }}
# Question:
{{ question }}
# Answer: <answer only>
Figure 12: A prompt template for GenRead, BM25,
Contriever, and Oracle. “{{ question }}” and “{{ con-
text }}” are replaced with an actual question and context,
respectively.
Generate a background document from
Wikipedia to answer the given question. {{
question }}"
Figure 13: A prompt template for GenRead passage
generation. “{{ question }}” is replaced with an actual
question. This prompt comes from its original paper
if questions with smaller S-R counts and smaller
S counts than their thresholds, otherwise we use
LMs with BM25. Then, we apply selective LMs
accordingly to the remaining questions in WiTQA.
We run the experiments with 5 different random
seeds to split the dataset and report the average and
standard deviations.
C.2.2
Results
Figure 14 shows that selective method achieves
better accuracy than Vanilla and BM25 across all
models. In particular for large models, the selective
memory integration improves 7.7%, 10.1%, and
8.1% over baselines for Llama-2-70B, GPT-3.5,
and GPT-4, respectively. As discussed in Section
4.3, the GPT series with retrievers frequently output
that the given contexts do not support the facts
related to questions, rather than directly answering
the questions. Consequently, their performance
with selective integration is lower or comparable to
that of Llama-2-70B.
Figure 15 illustrates the average retrieval ratio
of LMs with the selective memory integration over
5 runs. We observe a trend that the retrieval ratio
are shifting to smaller as the model size grows. For
Llama-2-70B-c GPT-3.5
GPT-4
0.0
0.5
Accuracy
62.8
64.0
68.9
69.1
65.2
61.6
74.4
±0.3
71.8
±0.3
74.5
±0.3
Vanilla
BM25
Selective
Figure 14: Accuracy of Vanilla, BM25, and LMs with
a selective retriever. Accuracy (%) is displayed inside
bars. As for selective LMs, we run 5 trials with different
random seed to split a dataset and show the standard
deviations.
Llama-2-70B-c
GPT-3.5
GPT-4
0.0
0.5
1.0
Retrieval Ratio
Retrieval
Not Retrieval
Figure 15: Average retrieval ratio of our selective mem-
ory integration method within 5 trials. “Retrieval” rep-
resents the ratio of answers sourced from LMs with
BM25, while “Not Retrieval” denotes the ratio of an-
swers sourced from Vanilla LMs.
example, GPT-4 uses a retriever for only 27.7% of
questions while Llama-2-70B uses a retriever for
43.0% of questions. This is mainly because larger
models can typically recall facts more correctly
than smaller models.
C.3
Retriever setup
Passage chunking
We use the llama-index to
chunk the Wikipedia documents into chunks with
chunk_size = 256 and chunk_overlap = 0.
Relation label
Count
country
1,269
sport
1,034
capital
1,032
capital of
754
genre
658
author
654
language used
639
country of citizenship
619
father
560
characters
554
religion
550
composer
534
occupation
518
publisher
495
director
479
place of birth
443
educated at
392
mother
351
industry
345
relative
331
screenwriter
278
producer
271
doctoral advisor
214
broadcast by
214
published in
210
location of first performance
209
cuisine
208
executive producer
208
color
207
medical condition
204
architectural style
203
director of photography
200
Table 4: Number of questions containing relations in
WiTQA.
Relation label
Count
country
1,979,521
sport
613,742
country of citizenship
278,973
place of birth
210,125
genre
169,649
capital
164,597
occupation
126,448
educated at
90,799
director
83,411
author
75,810
capital of
57,566
father
43,401
publisher
28,731
religion or worldview
23,986
composer
23,917
screenwriter
16,754
producer
16,578
language used
15,296
mother
10,348
industry
10,118
characters
9,783
architectural style
7,428
relative
4,327
doctoral advisor
2,544
published in
1,600
director of photography
1,254
location of first performance
1,086
broadcast by
874
color
787
medical condition
771
executive producer
707
cuisine
417
Table 5: Relation counts in all extracted triples in
Wikipedia abstracts.