Question:
Which challenges are associated with linear verbalization of KG facts?
A. Reduced evidence clarity due to lack of focus
B. Decreased computational efficiency
C. Redundant tokens from repeated entities or relations
D. Lack of semantic alignment with questions

Answer: AC

Despite their remarkable efficacy, existing
verbalization strategies for providing contextual
knowledge exhibit critical limitations. (1) Low
density of evidence: Both concatenation and lin-
earization of the facts (Baek et al., 2023a; Wu et al.,
2023) are highly likely to include duplicated en-
tities or relations due to limited flexibility; this
eventually degrades the density of useful evidence
within the contextual knowledge, hindering the abil-
ity to answer questions effectively. (2) Low clar-
ity of evidence: While contextual knowledge de-
scribes factual information, they often fail to high-
light the evidence necessary for answering ques-
tions. This lack of focus can lead to noise from
irrelevant facts, which can detrimentally impact
the LLM’s ability to provide accurate answers.

Evidence-Focused Fact Summarization for Knowledge-Augmented
Zero-Shot Question Answering
Sungho Ko∗, Hyunjin Cho∗, Hyungjoo Chae, Jinyoung Yeo, Dongha Lee†
Yonsei University
{k133324,cyberhyunjin,mapoout,jinyeo,donalee}@yonsei.ac.kr
Abstract
Recent studies have investigated utilizing
Knowledge Graphs (KGs) to enhance Ques-
tion Answering (QA) performance of Large
Language Models (LLMs), yet structured KG
verbalization remains challenging. Existing
methods, such as triple-form or free-form
textual conversion of triple-form facts, en-
counter several issues.
These include re-
duced evidence density due to duplicated en-
tities or relationships, and reduced evidence
clarity due to an inability to emphasize cru-
cial evidence.
To address these issues, we
propose EFSUM, an Evidence-focused Fact
Summarization framework for enhanced QA
with knowledge-augmented LLMs. We opti-
mize an open-source LLM as a fact summarizer
through distillation and preference alignment.
Our extensive experiments show that EFSUM
improves LLM’s zero-shot QA performance,
and it is possible to ensure both the helpfulness
and faithfulness of the summary.
1
Introduction
Large Language Models (LLMs) have shown re-
markable zero-shot abilities but often produce fac-
tual errors, known as hallucinations, particularly in
knowledge-intensive tasks like Question Answer-
ing (QA). This happens because the static knowl-
edge within LLM parameters may be incomplete,
incorrect, or outdated, failing to keep pace with
evolving real-world knowledge. Recent studies
remedy this by integrating external knowledge into
LLMs (Karpukhin et al., 2020b; Min et al., 2019).
As one form of external knowledge, Knowledge
Graphs (KGs) have been considered as the knowl-
edge source to augment LLMs for enhanced per-
formance in knowledge graph QA (KGQA) (Baek
et al., 2023a; Wu et al., 2023; Sen et al., 2023). The
key challenge of utilizing KGs, which consist of
a set of (head entity, relation, tail entity) triples,
∗Equal contribution
†Corresponding author
Pet
Sematary
Stephen
King
Author
S: The author of Pet
Sematary, Stephen King,
was educated at the
University of Maine …
✅Density
✅Clarity
Pet
Sematary
Jeff
Buhler
Screen
writer
Stephen
King
University
of Maine
Educa-
ted at
Q: Where did the author of
Pet Sematary go to college?
EFSum
A: University
of Maine
KG
LLM for QA
⋮
⋮
Figure 1: The QA pipeline based on LLM prompting,
augmented with relevant facts from KGs. Our fact sum-
marization improves both density and clarity of evidence
within contextual knowledge for enhanced QA.
is to bridge the modality gap between graphs and
text. Efforts on connecting the gap mostly fall into
either training additional layers to blend two repre-
sentations, or verbalizing graphs into texts. While
training fusion layers (Yasunaga et al., 2022a,b) can
make models expressive on two different modal-
ities, it takes expensive computaions, and needs
to be trained when KGs are updated.
On the
other hand, recent studies proposed verbalizing
KGs into text-form, without training LLMs for QA.
For instance, one strategy is simply concatenat-
ing the facts in their triple-form text (Baek et al.,
2023a), and another entails converting them into
semantically-coherent textual description (i.e., free-
form text) through the distillation of LLM’s ability
to generate text from KGs (Wu et al., 2023).
Despite their remarkable efficacy, existing
verbalization strategies for providing contextual
knowledge exhibit critical limitations. (1) Low
density of evidence: Both concatenation and lin-
earization of the facts (Baek et al., 2023a; Wu et al.,
2023) are highly likely to include duplicated en-
tities or relations due to limited flexibility; this
eventually degrades the density of useful evidence
within the contextual knowledge, hindering the abil-
ity to answer questions effectively. (2) Low clar-
ity of evidence: While contextual knowledge de-
scribes factual information, they often fail to high-
light the evidence necessary for answering ques-
arXiv:2403.02966v3  [cs.CL]  9 Oct 2024
tions. This lack of focus can lead to noise from
irrelevant facts, which can detrimentally impact the
LLM’s ability to provide accurate answers.
To address the aforementioned challenges, we
introduce a novel EFSUM framework, Evidence-
focused Fact Summarization for enhanced QA
with knowledge-augmented LLM prompting. The
key idea is transforming the set of facts into plausi-
ble and coherent summary while highlighting evi-
dence and filtering out noise given a question; this
ensures that the summaries maintain a high density
and clarity of evidence, facilitating effective QA
(Figure 1). The most straightforward solution to
this summarization is prompting LLMs with the de-
tailed instruction. However, LLM-generated sum-
maries often omit crucial evidence (such as answer
spans), resulting in information loss, or include in-
formation that cannot be inferred from retrieved
facts, leading to extrinsic hallucination.
For enhanced summary quality, we optimize an
open-source LLM as a fact summarizer in two
steps: LLM distillation and preference alignment
(Figure 3). During the first step, we train our fact
summarizer by using the reference summaries ob-
tained through LLM prompting. Subsequently, in
the second step, we refine our summarizer to bet-
ter align with the task-specific preference related
to QA. To this end, we introduce two preference
criteria for the summary candidates: helpfulness
evaluates LLMs can correctly answer the ques-
tion based on the summary, and faithfulness as-
sesses the factual consistency of the summary in
relation to the provided set of facts. By select-
ing pairs of preferred and dispreferred summaries
based on these criteria, we further fine-tune the
summarizer through direct preference optimization
(DPO) (Rafailov et al., 2023). In the end, EFSUM
is capable of generating summaries that are both
helpful for QA and faithful to the given facts.
Extensive experiments on two QA benchmark
datasets validate the effectiveness of our evidence-
focused fact summarization in improving LLM’s
zero-shot QA performance. EFSUM outperforms
other fact verbalization methods in two key settings:
(1) when fixing the token length (density, Section
4.2), and (2) when fixing the number of triples (clar-
ity, Section 4.3) within contextual knowledge. Ad-
ditionally, our approach enhances the helpfulness
and faithfulness of the generated fact summaries.
For reproducibility, our codes are publicly available
at https://github.com/kk13332488/EFSum.
2
Preliminaries
In this section, we introduce a KG-augmented zero-
shot QA pipeline, and provide analyses on the ver-
balized facts obtained by existing methods.
2.1
KG-Augmented LLM Prompting for QA
We focus on a QA approach that leverages LLMs’
zero-shot capability for answering the question,
enhanced with external knowledge from KGs.
Fact retrieval from knowledge graph.
The
first stage aims to retrieve question-associated facts
from KGs via entity linking, and then to select only
top-K ones based on their semantic relevance to
the input question. To select only the most relevant
facts, recent studies utilize semantic similarities be-
tween each fact and the question, employing either
a pretrained sentence encoder (Karpukhin et al.,
2020a; Xiong et al., 2020) or one fine-tuned specif-
ically for direct fact retrieval (Baek et al., 2023b).
In this work, we utilized the former strategy (Song
et al., 2020), if not stated.
Fact verbalization into various form text.
The
second stage is fact verbalization, which refers to
the task of transforming symbolic facts into tex-
tual strings, for feeding them into the LLM as the
contextual knowledge. The linear verbalization
simply concatenates the head, relation, and tail
texts in the triple while keeping the structured for-
mat (i.e., triple-form text) (Baek et al., 2023a), or
use manually-designed templates and heuristics for
linearization (Oguz et al., 2022; Ma et al., 2022).
On the other hand, the graph-to-text verbalization
transforms the input facts into the plausible and
coherent text by using a fine-tuned model (Ribeiro
et al., 2021) or prompting LLMs (Wu et al., 2023).
Fact injection for question answering.
The last
stage is prompting the LLM to generate the answer
with the verbalized facts. This process, also known
as knowledge-augmented LLM prompting for zero-
shot QA, gathers the output as the predicted answer.
To handle with insufficient evidence of knowledge,
the detailed instructions are provided to allow the
LLM to utilize its internal knowledge if needed.
The prompts are in Appendix D.2.
2.2
Analysis on Verbalized Facts
We provide a preliminary analysis of the contex-
tual knowledge obtained by each fact verbalization
method, evaluating their (1) density and (2) clar-
ity of evidence. Note that our proposed verbaliza-
The ratio of duplicated tokens
within the verbalized facts
Average position of the gold answer
within the verbalized facts
Semantic similarity of
the question to the verbalized facts
The length ratio of original facts
to the verbalized facts
KAPING
KG2Text
Rewrite
EFSumprompt (ours)
EFSumdistill (ours)
Figure 2: Analysis on each fact verbalization method.
tion method (denoted as EFSUM) summarizes the
symbolic facts into free-form texts, prioritizing the
evidence relevant to the question.
In Figure 2 Upper, it is evident that the number
of duplicated tokens is significantly higher in the
linearly verbalized texts (i.e., KAPING (Baek et al.,
2023a) and Rewrite (Wu et al., 2023)) compared
to the others. This strongly indicates that their out-
puts contain redundant information stemming from
the pre-defined relations within KGs. When con-
sidering the ratio of token lengths before and after
verbalization (i.e., compression rate), KAPING and
Rewrite either maintain or even increase the length,
despite maintaining the same amount of informa-
tion. Consequently, their evidence density remains
low or may even decrease during verbalization.
In Figure 2 Lower, the linear verbalization tends
to scatter obvious evidence (i.e., answer span) ran-
domly within the contextual knowledge. Their
placement seems to rely on the rank obtained
from fact retrieval, rather than being positioned
at the forefront of the verbalized texts for empha-
sis. Moreover, the average semantic similarity 1
of their verbalized facts with the question is lower
compared to other methods. This shows that their
outputs may include noisy or irrelevant information,
or they might not clearly highlight semantically rel-
evant evidence. In essence, clarity of evidence is
not adequately addressed in this approach.
3
EFSUM: Proposed Method
In this section, we present an Evidence-focused
Fact Summarization framework, named EFSUM,
1Average semantic similarity is calculated through the co-
sine similarity between embeddings encoded via MPNet.
which aims to effectively augment the set of rele-
vant facts to LLMs for zero-shot QA tasks.
3.1
LLM Prompting for EFSUM
The most straightforward implementation of
evidence-focused fact summarization is prompting
LLMs to generate a summary s given a question q
and its relevant facts F = {fk}K
k=1, focusing on the
evidence to answer the question. Thus, we present
EFSUMprompt to verbalize the facts with the help
of LLM’s zero-shot capability on summarization;
that is, s ∼pLLM(·|tsum, q, F), where tsum is the
prompt for summarization. Specifically, we instruct
LLMs to turn the input facts into the summary for
the scenarios where the summary serves as a con-
text to facilitate QA task. We utilized GPT-3.5-
turbo for generating summary of EFSUMprompt.
The detailed prompts are in Appendix D.2.
3.2
LLM Fine-Tuning for EFSUM
To enhance the quality of summaries, we propose
EFSUMdistill, a fact verbalization model based on
an open-source LLM,2 specifically fine-tuned for
evidence-focused fact summarization. For helpful
and faithful summary generation, EFSUMdistill is
optimized in two steps: (1) LLM distillation and
(2) preference alignment. Figure 3 illustrates the
overall EFSUMdistill framework.
3.2.1
Distillation of Fact Summarization
We first optimize our open-source model to gener-
ate diverse evidence-focused summaries given the
question and the set of symbolic facts. For opti-
mization, we augment the QA training dataset of
question-answer pairs (q, a) into X = {(q, a, F)},
where F = {fk}K
k=1 is the top-K retrieved facts
relevant to the question q.
Reference summary generation.
We utilize a
closed-source LLM (i.e., GPT-3.5-turbo) to ob-
tain reference fact summaries used for training our
summarizer. For each tuple (q, a, F) of a ques-
tion q and the relevant facts F, we prompt the
LLM to transform the set of facts F into a con-
cise textual description s that highlights the evi-
dence for the question q, which is same with EF-
SUMprompt. In the end, we construct the training
dataset D = {(q, a, F, s)}. The prompt used for
summary generation is in Table 5 of Appendix D.2.
2In this work, we choose Llama2-7B (Touvron et al., 2023)
as the backbone open-source LLM for EFSUMdistill.
Question
Teacher LLM
Broad Summary
⋯
Broad Summary
Broad Summary
✅Helpful ✅Faithful
❌Helpful ❌Faithful
Specific Summary
Diverse Summary Generation
Head
Tail
Relation
⋮
⋮
Head
Tail
Relation
Head
Tail
Relation
Broad Summary
Broad Summary
Prompt
Broad Summary
Question
Answer
✅Helpful ❌Faithful
Specific Summary
Helpfulness filter
Faithfulness filter
Answer?
Question
Summary
Facts
Summary
Entail?
Step 2: Preference Alignment for Helpful and Faithful Summarization
Step 1: Knowledge Distillation for Evidence-Focused Fact Summarization
Supervised Fine-Tuning
Prompt
EFSum
🤖
Student LLM
🤖
Direct Preference Optimization
Head
Tail
Relation
⋮
⋮
Head
Tail
Relation
Head
Tail
Relation
✅Helpful ✅Faithful
⋯
⋯
Specific Summary
❌Helpful ✅Faithful
Preferred
Dispreferred
Figure 3: The overall framework of EFSUMdistill. Our fact summarizer is trained to generate evidence-focused
summaries via LLM distillation, and then further optimized to align the QA-specific preference, which enhances the
helpfulness and faithfulness of its output summaries.
Supervised fine-tuning.
For each quadruplet
(q, F, s, a) from the dataset D, our summarizer θ
is optimized to generate s conditioned on q and F,
by using the causal language modeling objective:
LSFT = −
E
(q,a,F,s)∼D log pθ(s|q, F).
(1)
3.2.2
Alignment with Summary Preference
Our summarizer θ is now able to generate evidence-
focused summaries for a question and the relevant
facts, but its output summaries might be unhelpful
or unfaithful. Therefore, we additionally adopt
preference tuning to enhance EFSUMdistill so that
its summarization can align with the task-specific
preference in the context of knowledge-augmented
zero-shot QA; i.e., generating helpful and faithful
summaries, while avoiding the counterparts.
For preference tuning, we collect a set of pref-
erence pairs (q, a, F, s+, s−), where s+ and s−
respectively denote the preferred and dispreferred
summaries. To identify the preferred and dispre-
ferred summary for QA task, we first sample M
summary candidates {s′
m ∼pθ(·|q, F)}M
m=1 for
each tuple (q, a, F, s) ∈D using the summarizer
θ. Then, for constructing the preference pairs, we
adopt two summary filters (for checking helpful-
ness and faithfulness) and additional paraphrase
process. We form a preference pair by selecting the
answer-aware paraphrased candidate that passes
both filter as preferred, and the candidate that can-
not pass one of the filters as dispreferred.
Helpfulness filter.
The first filter examines the
helpfulness of each summary candidate in terms
of QA accuracy. That is, it checks whether the
summary candidate s′ is actually helpful to make
the LLM to find the correct answer, by comparing
the LLM’s generated answer a′ ∼pLLM(·|q, s′)
with the gold answer a.
Faithfulness filter.
The second filter focuses
on the faithfulness of each summary candidate.
We use the G-Eval approach (Liu et al., 2023) to
leverage LLM’s ability to evaluate the consistency
between the input facts and the given summary
in terms of hallucination. Precisely, the LLM is
prompted to examine whether or not the summary
contains unfaithful information, which cannot be
inferred from the given symbolic facts. Please refer
to the detailed prompt in Appendix D.2.
Broad-to-specific paraphrasing.
In addition,
we adopt the paraphrase process guided by the
gold answer a. This paraphrasing aims to obtain
high-quality summary by refining a broad focus
into a specific focus, using the given answer as
the main evidence. We prompt the LLM to para-
phrase the summary candidate s′ into s′′; {s′′
m ∼
pLLM(·|tparaphrase, s′, a)}M
m=1, where tparaphrase is
the prompt for paraphrasing. This candidate s′′ un-
dergoes another pass through the helpfulness filter
and the faithfulness filter. The resulting summary
obtained in this manner is much more focused on
the QA task than the initially obtained reference
summary, called as the broad summary. The de-
tailed prompt is provided in Appendix D.2
Direct Preference Optimization. Using the pref-
erence pairs P = {(q, a, F, s+, s−)}, we apply
Direct Preference Optimization (DPO) (Rafailov
et al., 2023) on our summarizer θ to train a
preference-tuned summarizer θ∗that minimizes
the following objective:
LDPO(θ∗; θ) =
−
E
(q,a,F,s+,s−)∼P log σ[r(q, F, s+) −r(q, F, s−)],
(2)
where r(q, F, s) = pθ∗(s|q,F)
pθ(s|q,F) . By optimizing the
model using preferred-dispreferred summary pairs,
our obtained model θ∗is trained to be more biased
towards helpful and faithful summary s+ while
avoiding unhelpful or unfaithful summary s−. Note
that θ has been specifically trained for each QA
model, as there are different preferences regarding
summary helpfulness across various QA models.
The following is an example of an output fact sum-
mary.
Question: where was george washington carver from?
Answer: Diamond
Facts: (George Washington Carver, occupation, bi-
ologist),(George Washington Carver, interested in,
botany),(George Washington Carver, occupation, univer-
sity teacher),(George Washington Carver, place of birth,
Diamond),(George Washington Carver, given name,
George), (George Washington Carver, field of work,
pedagogy),(George Washington Carver, relative, Moses
Carver),(George Washington Carver, place of birth,
United States of America),(George Washington Carver,
residence, Tuskegee),(George Washington Carver, occu-
pation, inventor)
Summary: George Washington Carver, the renowned
biologist, was born in Diamond, United States of Amer-
ica. He took great interest in botany and became a
university teacher, focusing on pedagogy. Additionally,
Carver contributed significantly as an inventor. His rel-
ative, Moses Carver, also had a close association with
him. Carver later resided in Tuskegee.
4
Experiments
In this section, we design our experiments to an-
swer the following research questions:
• RQ1: Does a high density of evidence in verbal-
ized facts contribute to QA accuracy?
• RQ2: Does a high clarity of evidence in verbal-
ized facts contribute to QA accuracy?
• RQ3: Can preference alignment enhance the gen-
eration of more helpful and faithful summaries?
4.1
Experimental Settings
Datasets.
WebQuestionsSP (WebQSP) (Yih
et al., 2016) is a KGQA dataset that filters ques-
tions from the WebQuestions (Berant et al., 2013)
dataset to include only those answerable via Free-
base, and provides SPARQL queries for them. For
convenience, we use WebQSP-WD (Sorokin and
Gurevych, 2018), in which each question from We-
bQSP is pre-linked to the Wikidata KG. We use
a test set comprising 1,033 examples for evalua-
tion. Mintaka (Sen et al., 2022) is a QA dataset
that encompasses eight different complexity types.
Most question-answer pairs can only be solved by
utilizing multi-hop reasoning or the attributes of
multiple entities. We use a test set of Mintaka
which has 4,000 examples for evaluation.
LLMs for zero-shot QA. To measure the efficacy
of EFSUM and other fact verbalization methods,
we utilized three different LLMs, GPT-3.5-turbo,
Flan-T5-XL (Chung et al., 2022), and Llama2-7B-
Chat (Touvron et al., 2023), for our zero-shot QA
evaluation. Note that Flan-T5-XL and Llama2-7B-
Chat are publicly available as open-source. We pro-
vide more details on the models in Appendix A.1.
Baseline methods. As the main baselines for fact
verbalization, we consider various approaches.
• No knowledge does not pass any knowledge con-
texts, encouraging the LLMs to use their internal
knowledge to answer the question.
• KAPING (Baek et al., 2023a) simply linearizes
top-K relevant facts as the triple-form text.
Triple-form text simply refers to the text that
is composed by concatenating triplet strings in
the form of (head, relation, tail).
• Rewrite (Wu et al., 2023) transforms facts into
the free-form text for each relation path with a
LLM. We utilize GPT-3.5-turbo to convert triples
into free-form text.
• KG2Text (Ribeiro et al., 2021) employs an
encoder-decoder model fined-tuned for the KG-
to-text task by using WebNLG (Gardent et al.,
2017) dataset. We utilize a fine-tuned T5-large
model3 as our base KG2Text model.
Evaluation metrics.
Our task can be catego-
rized as a generative KGQA. Following previous
work (Baek et al., 2023a; Wu et al., 2023), we use
accuracy as our evaluation metric. A score of 1
3https://public.ukp.informatik.tu-
darmstadt.de/ribeiro/graph2text/webnlg-t5-large.ckpt
Dataset
Methods
GPT-3.5-turbo
Flan-T5-XL
Llama2-7B-Chat
L=200
L=400
L=200
L=400
L=200
L=400
WebQSP
No knowledge
0.506
0.506
0.409
0.409
0.539
0.539
KAPING (Baek et al., 2023a)
0.507
0.538
0.391
0.439
0.517
0.519
KG2Text (Ribeiro et al., 2021)
0.476
0.476
0.316
0.321
0.439
0.481
Rewrite (Wu et al., 2023)
0.444
0.525
0.350
0.431
0.462
0.511
EFSUMprompt (Ours)
0.537
0.538
0.447
0.468
0.457
0.491
EFSUMdistill (Ours)
0.559
0.569
0.458
0.500
0.489
0.497
Mintaka
No knowledge
0.540
0.540
0.228
0.228
0.440
0.440
KAPING (Baek et al., 2023a)
0.539
0.539
0.269
0.279
0.402
0.407
KG2Text (Ribeiro et al., 2021)
0.492
0.491
0.234
0.234
0.377
0.378
Rewrite (Wu et al., 2023)
0.515
0.521
0.280
0.288
0.394
0.386
EFSUMprompt (Ours)
0.496
0.491
0.312
0.321
0.423
0.418
EFSUMdistill (Ours)
0.474
0.449
0.326
0.338
0.405
0.406
Table 1: QA accuracy of the LLMs based on various fact verbalization. We limit the maximum token length of
contextual knowledge to L = 200 and 400. The best and second-best results are in bold and underlined, respectively.
Datasets
Methods
GPT-3.5-turbo
Flan-T5-XL
Llama2-7B-Chat
Random
Popular
MPNet
Random
Popular
MPNet
Random
Popular
MPNet
WebQSP
No knowledge
0.506
0.506
0.506
0.409
0.409
0.409
0.539
0.539
0.539
KAPING (Baek et al., 2023a)
0.441
0.437
0.538
0.297
0.329
0.439
0.476
0.490
0.519
KG2Text (Ribeiro et al., 2021)
0.469
0.468
0.476
0.317
0.276
0.321
0.465
0.451
0.481
Rewrite (Wu et al., 2023)
0.473
0.445
0.525
0.323
0.348
0.431
0.458
0.439
0.511
EFSUMprompt (Ours)
0.542
0.534
0.538
0.443
0.442
0.468
0.477
0.472
0.491
EFSUMdistill (Ours)
0.475
0.539
0.569
0.500
0.505
0.500
0.457
0.488
0.497
Mintaka
No knowledge
0.540
0.540
0.540
0.228
0.228
0.228
0.440
0.440
0.440
KAPING (Baek et al., 2023a)
0.553
0.516
0.539
0.201
0.198
0.279
0.417
0.398
0.407
KG2Text (Ribeiro et al., 2021)
0.505
0.500
0.492
0.220
0.235
0.234
0.421
0.389
0.378
Rewrite (Wu et al., 2023)
0.527
0.524
0.515
0.230
0.224
0.288
0.393
0.374
0.386
EFSUMprompt (Ours)
0.454
0.492
0.496
0.213
0.215
0.321
0.390
0.392
0.418
EFSUMdistill (Ours)
0.427
0.425
0.474
0.292
0.243
0.338
0.397
0.393
0.406
Table 2: QA accuracy of the LLMs based on various fact verbalization, with different fact retrieval strategies
(i.e., random facts, popular facts, and question-relevant facts). We limit the maximum token length of contextual
knowledge to L = 400. The best and second-best results are in bold and underlined, respectively.
is assigned if at least one among multiple correct
answers is present in the response text of the QA
model; otherwise, the score is 0.
Relevant fact retrieval.
In retrieving question-
related facts, as described in the Section 2.1, we
employ MPNet (Song et al., 2020) to retrieve only
the top-K triples among given KG, with the highest
semantic similarity to the question representation,
following Baek et al. (2023a). Since processing
entire KGs is impractical, we focus on retrieving
information from the n-hop neighbors of question
entities within a KG. The value of n is given by
the specific KGQA dataset to answer the question.
In our experiment, n is set to 1 for WebQSP and 2
for Mintaka. When calculating semantic similarity,
we use the linear verbalization approach, which
involves combining the subject, relation, and object
texts from the triple.
4.2
Effectiveness of Dense Evidence (RQ1)
To examine the impact of dense evidence within
verbalized facts on the final QA performance, we
evaluate the LLM’s QA accuracy while imposing
restrictions on the maximum token lengths L of
contextual knowledge. This implies that the num-
ber of facts included in the contextual knowledge
varies depending on fact verbalization methods.
Effect of knowledge augmentation. First of all,
we observe that knowledge augmentation for zero-
shot QA does not always produce positive results.
Knowledge augmentation cannot be helpful in two
scenarios: where the model’s ability to ground in-
Dataset
Methods
GPT-3.5-turbo
Flan-T5-XL
Llama2-7B-Chat
K=10
K=30
K=10
K=30
K=10
K=30
WebQSP
No knowledge
0.617
0.607
0.498
0.451
0.646
0.628
KAPING (Baek et al., 2023a)
0.777
0.771
0.643
0.738
0.668
0.699
KG2Text (Ribeiro et al., 2021)
0.589
0.608
0.467
0.457
0.409
0.536
Rewrite (Wu et al., 2023)
0.628
0.728
0.533
0.664
0.594
0.688
EFSUMprompt (Ours)
0.788
0.755
0.629
0.711
0.497
0.571
EFSUMdistill (Ours)
0.786
0.783
0.644
0.741
0.599
0.666
Mintaka
No knowledge
0.810
0.788
0.492
0.444
0.783
0.719
KAPING (Baek et al., 2023a)
0.912
0.869
0.723
0.673
0.832
0.808
KG2Text (Ribeiro et al., 2021)
0.879
0.768
0.536
0.491
0.799
0.727
Rewrite (Wu et al., 2023)
0.901
0.875
0.720
0.691
0.843
0.792
EFSUMprompt (Ours)
0.920
0.887
0.742
0.735
0.849
0.806
EFSUMdistill (Ours)
0.893
0.824
0.745
0.719
0.852
0.826
Table 3: QA accuracy of the LLMs based on fact verbalization methods. We fix the number of facts to K = 10 and
30. The best and second-best results are in bold and underlined, respectively.
put knowledge is lacking, or where the retrieved
knowledge is noisy while the QA model’s internal
knowledge is sufficient. In Table 1, when Llama2-
7B-Chat was used as the QA model, it demon-
strates higher performance under the “No knowl-
edge” condition across both datasets (i.e., WebQSP
and Mintaka) compared to other baselines. This is
indicative of Llama2-7B-Chat’s limited capability
in utilizing the provided knowledge.
Comparison with other baselines. We first com-
pare the performance of various fact verbalization
methods at L = 200 and 400. In Table 1, for most
cases, EFSUM shows superior performance over
the majority of baseline approaches. Considering
the ratio of lengths before/after verbalization (in
Figure 2), this clearly indicates that EFSUM can
encapsulate more intensive and useful information
within shorter summaries. Notably, the effective-
ness of our approach is more evident when the
length of knowledge decreases from L = 400 to
200. This suggests that EFSUM remains highly
effective even in contexts when the utilization of
extremely concise knowledge is required.
Compatibility with various retrievers.
We in-
vestigate the QA performance using different types
of fact retrieval, to assess the robustness across
various knowledge qualities: randomly selected
knowledge (Random), the knowledge possessing
the most frequently occurring relation (Popular),
and question-relevant knowledge (MPNet). In Ta-
ble 2, EFSUM achieves the highest accuracy across
most datasets and QA models, regardless of the
retriever used, which implies that our method can
extract useful facts from noisy input triples.
Figure 4: Summary-level and answer-level QA accu-
racies with respect to the number of relevant facts on
WebQSP (Upper) and Mintaka (Lower), respectively.
4.3
Effectiveness of Clear Evidence (RQ2)
To examine the effectiveness of clear evidence
within verbalized facts on the final QA perfor-
mance, we assess the LLM’s QA accuracy only
on the test tuples (q, a, F) where the facts F fully
contain the ground-truth answer span a. This ex-
perimental setup allows us to investigate how effec-
tively each fact verbalization method converts F
into a textual string without overlooking evidence
a, thereby enabling correct answers.
Comparison with other baselines. To examine
the effectiveness of EFSUM in terms of evidence
clarity, we evaluate the LLM’s zero-shot QA accu-
racy when the number of facts is K = 10 and 30.
In Table 3, EFSUM shows the highest performance
across most datasets, showing consistently leading
results for both K values. Nevertheless, we notice
the presence of uncontrolled model inclination. For
KG2Text
Rewrite
EFSumprompt
EFSumdistill
KG2Text
EFSumprompt
EFSumdistill
Rewrite
Figure 5: Two quality metrics of verbalized facts.
example, KAPING consistently exhibits the high-
est performance when Llama2-7B-Chat is utilized
as the QA model for the WebQSP dataset. This
might be due to the model’s inclination towards a
specific knowledge format.
Robustness of EFSUM across various K.
To
assess the robustness of EFSUM, we increase the re-
trieved number of facts K from 10 to 150 and mea-
sure the performance of each baseline at the answer-
level and summary-level. At the answer-level, we
evaluate the extent to which the answers are con-
tained within the responses generated by the QA
model using summaries. In case of the summary-
level, we evaluate whether the answer is included
within the verbalized knowledge produced by the
method. In Figure 4, across both datasets and the
majority of K values, EFSUMdistill consistently
outperforms, especially in answer-level accuracy,
except for KAPING, which inherently achieves a
summary level accuracy of 1 by simple lineariza-
tion of facts.
4.4
Effect of Preference Alignment (RQ3)
Helpfulness and faithfulness.
We examine the
helpfulness and faithfulness of the verbalized facts
generated by each verbalization method.4 Note that
helpfulness is calculated through summary-level
accuracy, while faithfulness is calculated as 1 - (hal-
lucination occurrence rate). That is, a method with
a lower rate of hallucination occurrences possesses
higher faithfulness. Figure 5 illustrates that EF-
SUMdistill achieves superior helpfulness and faith-
fulness compared to other baselines, including EF-
SUMprompt. In other words, EFSUMdistill is less
likely to generate summaries of hallucination and is
capable of incorporating more correct answers into
the summaries compared to other baselines. More-
over, it validates that using the two filters in the
broad-to-specific paraphrasing process definitively
aids in improving both faithfulness and helpfulness.
4We opt not to include KAPING in the plot, as its linear
verbalization does not alter the content of the facts.
5
Related Work
5.1
KG-Augmented LLM Prompting
To supplement the incomplete internal knowledge
of LLMs, recent research has been exploring knowl-
edge augmentation methods using prompting. Vari-
ous types of information can be utilized for prompt-
ing knowledge, notably KGs. Recently, Baek et al.
(2023a); Wu et al. (2023) utilize triple-form facts
from KGs for knowledge augmentation for LLM-
prompting. However, the knowledge injected into
LLMs by these methods is not focused on the ques-
tion. In other words, the semantics of the question
are not considered during the process of deriving
the final knowledge from the given triples. Conse-
quently, the final knowledge derived through each
method may inevitably have low density and clarity
from an evidence perspective.
5.2
Knowledge Graph Question Answering
Studies exploring the integration of additional
knowledge extracted from KGs, represented as
subgraphs, are divided into two main approaches.
The first approach utilizes semantic parsing tech-
niques (Bao et al., 2016; Luo et al., 2018), which
enable the extraction of executable queries from the
KG by using contextual information as a parsing
reference. Alternatively, the information retrieval
approach involves encoding assimilated informa-
tion using techniques like Graph Neural Networks
(GNNs). Several recent studies (Yasunaga et al.,
2022a,b; Zhang et al., 2022) propose a learning
framework that combines GNNs and LMs, allow-
ing concurrent utilization of textual data and KG.
Besides, approaches that project text embeddings to
graph embeddings (Razzhigaev et al., 2023) strug-
gle to avoid hallucinations by LLMs. In contrast to
the conventional KGQA approaches that aim to di-
rectly identify the answer entity within KGs, in this
work, we mainly focus on how the KG-retrieved
facts can be effectively utilized within the zero-shot
QA capability of LLMs.
6
Conclusion
In this paper, we explore methods to enhance
the zero-shot QA performance of LLMs by aug-
menting knowledge from KGs. We introduce a
novel summarization framework, called EFSUM,
which transforms a set of facts into summary with
high density and clarity of evidence for answer-
ing the question. To achieve this, we optimize an
open-source LLM as a fact summarizer, leveraging
a teacher LLM’s summarization capabilities and
aligning its outputs with QA-specific preferences.
Our experiments show that EFSUM significantly
improve QA accuracy across various LLMs com-
pared to other fact verbalization approaches. Fur-
thermore, serving as an independent summarization
module, it generates helpful and faithful summaries
based on relevant facts and target questions.
7
Limitation
Despite our discoveries and improvements, we
must acknowledge certain limitations in our work
and potential areas for future research.
To begin with, the accuracy, which is the metric
used in our experiments has the potential to over-
estimate the correctness of responses, even if the
response does not accurately convey the intended
semantic meaning. This discrepancy can occur
because the metric simply verifies the existence
of the answer entity, regardless of whether it is
contextually appropriate. Unlike semantic parsing
KGQA, which involves retrieving entities from the
KG, or multiple-choice KGQA, where the answer
is chosen from several options, evaluating metrics
for generative KGQA remains an open field that
warrants further investigation.
Secondly, there are cases where the tendency of
LLMs to favor a certain fact verbalization method
becomes overwhelming and difficult to manage.
As demonstrated in Tables 2 and 3, with the exam-
ple of Llama2-7B-Chat on the WebQSP dataset, it
has been observed that certain models may have a
inclination for specific knowledge formats in partic-
ular datasets. Consequently, while our summarizer
generally demonstrates good performance, control-
ling performance may become challenging when
a specific model has a strong inclination towards a
particular knowledge format.
Lastly, it is important to note that the perfor-
mance of our proposed summarizer can be influ-
enced by the performance of the retriever. As can
be seen in Table 2, using a better retriever can
lead to higher performance. While the off-the-shelf
model we used (i.e. MPNet) demonstrates retriev-
ing capabilities based on the semantic similarity
between questions and facts, it’s difficult to assert
that it is a flawless retriever. For example, in a
2-hop dataset(i.e. Mintaka), it tends to retrieve
1-hop neighbors more than 2-hops even if it is ir-
relevant. This is because an answer entity in 2-hop
neighbors is unseen in given question, so that a
retrieval model may measure question entities in
1-hop neighbors more similar. Therefore, we are
currently conducting further research to propose
not only a more powerful summarizer but also a
more flawless retrieving method simultaneously.
8
Ethical Consideration
Throughout our research, we thoroughly explore
our methodology using an open-source dataset,
chosen to ensure transparency and integrity in our
work. It is important to acknowledge the inherent
potential for biases within our summarizer, which
relies on LLMs, and which may inadvertently re-
flect prevailing social biases. It is crucial to notice
that our method is not intended to inflict harm upon
any individuals or groups.
9
Acknowledgements
This work was supported by the IITP grants funded
by the Korea government (MSIT) (No. RS-2020-
II201361; RS-2024-00457882, AI Research Hub
Project), and the NRF grant funded by the Korea
government (MSIT) (No. RS-2023-00244689).
References
Jinheon Baek, Alham Aji, and Amir Saffari. 2023a.
Knowledge-augmented language model prompting
for zero-shot knowledge graph question answering.
In Proceedings of the First Workshop on Matching
From Unstructured and Structured Data (MATCH-
ING 2023), pages 70–98, Toronto, ON, Canada. As-
sociation for Computational Linguistics.
Jinheon Baek, Alham Fikri Aji, Jens Lehmann, and
Sung Ju Hwang. 2023b. Direct fact retrieval from
knowledge graphs without entity linking. In Proceed-
ings of the 61st Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers),
pages 10038–10055, Toronto, Canada. Association
for Computational Linguistics.
Junwei Bao, Nan Duan, Zhao Yan, Ming Zhou, and
Tiejun Zhao. 2016. Constraint-based question an-
swering with knowledge graph. In Proceedings of
COLING 2016, the 26th International Conference on
Computational Linguistics: Technical Papers, pages
2503–2514, Osaka, Japan. The COLING 2016 Orga-
nizing Committee.
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy
Liang. 2013. Semantic parsing on Freebase from
question-answer pairs. In Proceedings of the 2013
Conference on Empirical Methods in Natural Lan-
guage Processing, pages 1533–1544, Seattle, Wash-
ington, USA. Association for Computational Linguis-
tics.
Hyung Won Chung, Le Hou, Shayne Longpre, Barret
Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi
Wang, Mostafa Dehghani, Siddhartha Brahma, et al.
2022. Scaling instruction-finetuned language models.
arXiv preprint arXiv:2210.11416.
Claire Gardent, Anastasia Shimorina, Shashi Narayan,
and Laura Perez-Beltrachini. 2017. The WebNLG
challenge: Generating text from RDF data. In Pro-
ceedings of the 10th International Conference on
Natural Language Generation, pages 124–133, San-
tiago de Compostela, Spain. Association for Compu-
tational Linguistics.
Filip Grali´nski, Anna Wróblewska, Tomasz Stanisławek,
Kamil Grabowski, and Tomasz Górecki. 2019.
GEval: Tool for debugging NLP datasets and mod-
els.
In Proceedings of the 2019 ACL Workshop
BlackboxNLP: Analyzing and Interpreting Neural
Networks for NLP, pages 254–262, Florence, Italy.
Association for Computational Linguistics.
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick
Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and
Wen-tau Yih. 2020a. Dense passage retrieval for
open-domain question answering. In Proceedings
of the 2020 Conference on Empirical Methods in
Natural Language Processing (EMNLP), pages 6769–
6781, Online. Association for Computational Lin-
guistics.
Vladimir Karpukhin, Barlas O˘guz, Sewon Min, Patrick
Lewis, Ledell Yu Wu, Sergey Edunov, Danqi Chen,
and Wen tau Yih. 2020b.
Dense passage re-
trieval for open-domain question answering. ArXiv,
abs/2004.04906.
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang,
Ruochen Xu, and Chenguang Zhu. 2023. G-eval:
NLG evaluation using gpt-4 with better human align-
ment. In Proceedings of the 2023 Conference on
Empirical Methods in Natural Language Processing,
pages 2511–2522, Singapore. Association for Com-
putational Linguistics.
Kangqi Luo, Fengli Lin, Xusheng Luo, and Kenny Zhu.
2018. Knowledge base question answering via encod-
ing of complex query graphs. In Proceedings of the
2018 Conference on Empirical Methods in Natural
Language Processing, pages 2185–2194, Brussels,
Belgium. Association for Computational Linguistics.
Kaixin Ma, Hao Cheng, Xiaodong Liu, Eric Nyberg,
and Jianfeng Gao. 2022.
Open domain question
answering with a unified knowledge interface. In
Proceedings of the 60th Annual Meeting of the As-
sociation for Computational Linguistics (Volume 1:
Long Papers), pages 1605–1620.
Sewon Min, Danqi Chen, Luke Zettlemoyer, and Han-
naneh Hajishirzi. 2019. Knowledge guided text re-
trieval and reading for open domain question answer-
ing. ArXiv, abs/1911.03868.
Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan
Peshterliev, Dmytro Okhonko, Michael Schlichtkrull,
Sonal Gupta, Yashar Mehdad, and Scott Yih. 2022.
UniK-QA: Unified representations of structured and
unstructured knowledge for open-domain question
answering. In Findings of the Association for Compu-
tational Linguistics: NAACL 2022, pages 1535–1546,
Seattle, United States. Association for Computational
Linguistics.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano
Ermon, Christopher D Manning, and Chelsea Finn.
2023. Direct preference optimization: Your language
model is secretly a reward model. arXiv preprint
arXiv:2305.18290.
Anton Razzhigaev, Mikhail Salnikov, Valentin Malykh,
Pavel Braslavski, and Alexander Panchenko. 2023.
A system for answering simple questions in multiple
languages. In Proceedings of the 61st Annual Meet-
ing of the Association for Computational Linguistics
(Volume 3: System Demonstrations), pages 524–537,
Toronto, Canada. Association for Computational Lin-
guistics.
Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich
Schütze, and Iryna Gurevych. 2021. Investigating
pretrained language models for graph-to-text genera-
tion. In Proceedings of the 3rd Workshop on Natural
Language Processing for Conversational AI, pages
211–227, Online. Association for Computational Lin-
guistics.
Priyanka Sen, Alham Fikri Aji, and Amir Saffari.
2022. Mintaka: A complex, natural, and multilin-
gual dataset for end-to-end question answering. In
Proceedings of the 29th International Conference
on Computational Linguistics, pages 1604–1619,
Gyeongju, Republic of Korea. International Com-
mittee on Computational Linguistics.
Priyanka Sen, Sandeep Mavadia, and Amir Saffari. 2023.
Knowledge graph-augmented language models for
complex question answering.
In Proceedings of
the 1st Workshop on Natural Language Reasoning
and Structured Explanations (NLRSE), pages 1–8,
Toronto, Canada. Association for Computational Lin-
guistics.
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-
Yan Liu. 2020. Mpnet: Masked and permuted pre-
training for language understanding. Advances in
Neural Information Processing Systems, 33:16857–
16867.
Daniil Sorokin and Iryna Gurevych. 2018. Modeling se-
mantics with gated graph neural networks for knowl-
edge base question answering. In Proceedings of the
27th International Conference on Computational Lin-
guistics, pages 3306–3317, Santa Fe, New Mexico,
USA. Association for Computational Linguistics.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Al-
bert, Amjad Almahairi, Yasmine Babaei, Nikolay
Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti
Bhosale, et al. 2023.
Llama 2:
Open founda-
tion and fine-tuned chat models.
arXiv preprint
arXiv:2307.09288.
Yike Wu, Nan Hu, Guilin Qi, Sheng Bi, Jie Ren, An-
huan Xie, and Wei Song. 2023. Retrieve-rewrite-
answer: A kg-to-text enhanced llms framework for
knowledge graph question answering. arXiv preprint
arXiv:2309.11206.
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang,
Jialin Liu, Paul N Bennett, Junaid Ahmed, and
Arnold Overwijk. 2020. Approximate nearest neigh-
bor negative contrastive learning for dense text re-
trieval. In International Conference on Learning
Representations.
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren,
Xikun Zhang, Christopher D Manning, Percy Liang,
and Jure Leskovec. 2022b.
Deep bidirectional
language-knowledge graph pretraining.
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut,
Percy Liang, and Jure Leskovec. 2022a. Qa-gnn:
Reasoning with language models and knowledge
graphs for question answering.
Wen-tau Yih, Matthew Richardson, Chris Meek, Ming-
Wei Chang, and Jina Suh. 2016. The value of se-
mantic parse labeling for knowledge base question
answering. In Proceedings of the 54th Annual Meet-
ing of the Association for Computational Linguistics
(Volume 2: Short Papers), pages 201–206, Berlin,
Germany. Association for Computational Linguis-
tics.
Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga,
Hongyu Ren, Percy Liang, Christopher D Manning,
and Jure Leskovec. 2022. Greaselm: Graph reason-
ing enhanced language models for question answer-
ing. arXiv preprint arXiv:2201.08860.
A
Implementation Details
A.1
LLMs for Zero-Shot QA
• Flan-T5 is a variant model grounded in the T5
architecture. T5, an encoder-decoder model,
is trained on a text-to-text dataset featuring a
diverse array of objectives. Flan-T5 represents
an evolution of T5 through the process of in-
struction tuning, enhancing its performance
by aligning it more closely with specific in-
structional contexts. We employ Flan-T5-XL
for our QA model.
• Llama2-7B-Chat Llama2 is the developed
version of Llama1(Touvron et al., 2023). For
the purpose of achieving more accurate mea-
surement of summarizer performance, we em-
ploy the Llama2-7B-chat, which is specifi-
cally optimized for conversational contexts.
• GPT-3.5-turbo, developed by OpenAI, stands
as a prominent closed-source model renowned
for its high performance, making it particu-
larly well-suited for application in QA models.
Due to GPT-3.5-turbo being closed-source,
we utilize the API provided by OpenAI5.
A.2
Faithfulness Evaluation
To achieve a more faithful summary, we under-
take distillation using data meticulously purged
of summaries affected by hallucinations. For this
purpose, we eliminate hallucinations from both
broad and specific summaries based on prompts
provided by G-eval (Grali´nski et al., 2019). We re-
vise the G-eval-provided prompts with QA-specific
instructions and generated auto Chain of Thought
(CoT). This auto-generated CoT are then utilized
as evaluation criteria to ascertain the factual con-
sistency between the generated summaries and the
facts, marking them as true or false. For a more
accurate assessment of faithfulness, we measure
hallucination using GPT-4, a more advanced model
than GPT-3.5-turbo, which was used for reference
summary generation and paraphrasing. The faith-
fulness of the method is defined as (1 -hallucination
occurrence rate).
A.3
Reference Summary Generation
For the purpose of generating reference summaries
conducive to distillation, we utilize GPT-3.5-turbo.
Given the scarcity of training data available within
each dataset, we embark on a strategy of data aug-
mentation to distill a more diverse array of sum-
mary cases. Through GPT-3.5-turbo, five sum-
maries are augmented for each sample, and to foster
an even greater diversity of summary instances, we
adjust the decoding temperature, to 1.1.
A.4
Paraphrased Summary Generation
For generating specific summaries for DPO train-
ing, we conduct paraphrasing. Summary from the
reference summaries that passed through the help-
fulness and faithfulness filters is transformed from
broader summary to specific summary through
paraphrasing. For this paraphrasing endeavor, GPT-
3.5-turbo is once again employed. To select the
most effective summary among a variety of para-
phrased summaries, multiple paraphrased sum-
maries are generated for each sample. To ensure
diversity, the decoding temperature is set to 1.1.
5https://openai.com/api/
A.5
Distilled Summary Generation
In the generation of the final summary through the
EFSUM, the temperature is set to 0.1. While it is
appropriate to elevate the temperature to consider
a wider range of candidates during the creation of
a augmented dataset for distillation, we conclude
that for generating summaries for QA inference,
it is more important to promote consistency over
diversity in the summaries.
B
Experimental Settings
B.1
Datasets
• WebQuestionsSP (WebQSP) (Berant et al.,
2013; Yih et al., 2016) Semantic Parses
Dataset, abbreviated to WebQSP, is a KGQA
dataset providing SPARQL queries which al-
lows a direct retrieval from Freebase KG. Due
to the cessation of updates to Freebase we
adopt Wikidata as the foundational KG. As
we mentioned in section
4.1, we utilized
WebQSP-WD (Sorokin and Gurevych, 2018),
a dataset that offers questions from WebQSP
pre-linked to the Wikidata KG.
• Mintaka (Sen et al., 2022)
A KGQA dataset
collected from Wikidata, with 8 complex-
ity types, including ‘Count’, ‘Comparative’,
‘Superlative’, ‘Ordinal’, ‘Multi-hop’, ‘Inter-
section’, ‘Difference’, ‘Yes/No’, ‘Generic’.
Question-Answer pairs are collected from
Wikidata entities. This dataset is multilingual,
and we use English datasets in this work.
B.2
Various Retrievers
• Random Knowledge is a knowledge augmen-
tation method that entails the selection of K
arbitrary facts to serve as the final knowledge.
• Popular Knowledge employs a method
wherein triples are organized based on the re-
leation frequency of their occurrence among
one-hop neighbor triples. Then top K triples,
sorted by the prevalence of their relations, are
utilized as the final knowledge.
• MPNet is a retrieval method predicated on
the semantic similarity between questions and
triples. Each triple in the triple set is compared
to the question based on the cosine similarity
between their MPNet representations. The K
triples exhibiting the highest similarity to the
Dataset
Method
Acc
WebQSP
EFSUMdistill
0.500
w/o Paraphrase
0.477
w/o Helpfulness
0.453
w/o All Filters, Parapharase
0.469
Mintaka
EFSUMdistill
0.338
w/o Paraphrase
0.296
w/o Helpfulness
0.289
w/o All Filters, Parapharase
0.299
Table 4: Ablation study on different filters and paraphrasing.
The best and second-best results are in bold and underlined,
respectively.
question are then selected as the final knowl-
edge.
C
Additional Experiment Results
C.1
Ablation Study
To investigate the effect of different filters and para-
phrasing approach on EFSUM, in Table 4, we eval-
uate the performance of EFSUM on ablation study.
For the experimental setting, we used Flan-T5-XL
as a QA model on WebQSP and Mintaka with a
maximum token length of contextual knowledge as
L = 400. Each row indicates whether each filter
or paraphrasing approach is used or not. ‘w/o All
Filters, Paraphrase’means the model is trained with-
out using any filtering or paraphrasing technique.
Table 4 demonstrates that the performance of the
distilled model drops filters are removed or the para-
phrasing stage is omitted. This result indicates that
EFSUM shows the best performance when all pro-
cesses are put together. When the helpfulness filter
is removed, some summaries that do not aid the QA
task get mixed into the chosen set during DPO train-
ing. This disrupts the model’s optimization process,
leading to a performance decline. Consequently,
removing the helpfulness filter results in the most
critical performance drop. The paraphrasing stage
also plays a crucial role. Without paraphrasing, the
ability to highlight important information within
paragraphs decreases. As a result, key information
in the summary becomes distracted, making it dif-
ficult for the QA model to digest the knowledge.
This inevitably leads to a performance decline.
C.2
Generalization Ability
We examine the generalization capability of EF-
SUM for unseen datasets. In Table 5, We inves-
tigate the cross-dataset experiments on Flan-T5-
XL with a maximum token length of contextual
knowledge as L = 400 (i.e. compatible to Ta-
Method
Trained on
Generate Summaries on
Acc
EFSUMdistill
Mintaka
WebQSP
0.455
EFSUMdistill
WebQSP
Mintaka
0.281
KAPING
WebQSP
WebQSP
0.439
Rewrite
Mintaka
Mintaka
0.288
Table 5: Generalization capability of EFSUMdistill for un-
seen datasets.
ble 1). We use EFSUMdistill that generates the
summary for Mintaka trained on WebQSP, also
vice versa. Referring to Table 1, the most supe-
rior baselines scores 0.439 and 0.288 for WebQSP
and Mintaka, respectively. And EFSUMdistill that
trained on unseen dataset scores 0.455 and 0.281
for WebQSP and Mintaka, respectively. This re-
sult shows that our methods outperform the most
of baselines and are competitive to the most ef-
fective baselines. This indicates that the proposed
evidence-focused approach can effectively summa-
rize the evidence even on unseen datasets.
D
Qualitative Examples
D.1
Case Study
In Table 6, we present an example of verbalized
facts generated by each method. EFSUM empha-
sizes evidence necessary to answer the question
(i.e., Emilio Estevez’s father is Martin Sheen.) at
the beginning, while excluding irrelevant details for
brevity, as opposed to other baselines. Duplicated
relations (i.e., relations of siblings) are aggregated,
and it is clear that our method is more compact than
KAPING and Rewrite. Comparing to KG2Text, it
also sums up the given triples, but reduces a loss of
essential information.
D.2
Prompts
We provide various LLM prompts, used for (1)
evidence-focused summarization, (2) knowledge-
augmented zero-shot QA, (3) paraphrase of sum-
mary candidates, and (4) faithfulness evaluation.
Question: Who is Emilio Estevez’s father?
Answer: Martin Sheen
KAPING: (Emilio Estevez’s, family name, Estévez), (Emilio Estevez’s, given name, Emilio), (Emilio Estevez’s,
father, Martin Sheen), (Emilio Estevez’s, occupation, actor), (Joe Estevez, relative, Emilio Estevez), (Ramón
Estévez, sibling, Emilio Estevez), (Emilio Estevez’s, relative, Joe Estevez), (Ramón Estévez, father, Martin
Sheen), (Martin Sheen, child, Emilio Estevez), (Emilio Estevez’s, date of birth, +1962-05-12T00:00:00Z), (Joe
Estevez, relative, Ramón Estévez), (Emilio Estevez’s, birth name, Emilio Estevez Sheen), (Emilio Estevez’s,
sibling, Ramón Estévez), (Ramón Estévez, relative, Joe Estevez), (Ramón Estévez, family name, Estévez),
(Emilio Estevez’s, occupation, film producer), (Renée Estévez, sibling, Emilio Estevez), (Charlie Sheen, sibling,
Emilio Estevez), (Janet Sheen, child, Emilio Estevez), (Martin Sheen, child, Ramón Estévez), (Joe Estevez,
family name, Estevez), (Martin Sheen, family name, Estévez), (Emilio Estevez’s, described by source, Obalky
knih.cz), (Ramón Estévez, occupation, television actor), (Emilio Estevez’s, occupation, film director), (Emilio
Estevez’s, country of citizenship, United States of America), (Emilio Estevez’s, occupation, writer), (Estévez,
said to be the same as, Estevez), (Emilio Estevez’s, sibling, Renée Estévez), (Emilio Estevez’s, place of birth,
New York City)
KG2Text the family name of emilio estevez is estévez . he was born in new york city and is from the united
states . he is the father of two children , both of whom are children . he is the actor , writer and film director . he
is the brother of charlie sheen and his sister is renée .
Rewrite: Emilio Estevez’s family name is Estévez. Emilio Estevez’s given name is Emilio. Emilio Estevez’s
father is Martin Sheen. Emilio Estevez is an actor. Joe Estevez is a relative of Emilio Estevez. Ramón Estévez is
a sibling of Emilio Estevez. Emilio Estevez is related to Joe Estevez as they are relatives. Ramón Estévez is
the father of Martin Sheen. Martin Sheen is the father of Emilio Estevez. Joe Estevez is a relative of Ramón
Estévez. Emilio Estevez has a sibling named Ramón Estévez. Ramón Estévez is a relative of Joe Estevez. Ramón
Estévez’s family name is Estévez. Emilio Estevez is a film producer. Renée Estévez is the sibling of Emilio
Estevez. Charlie Sheen is the sibling of Emilio Estevez. Janet Sheen is the mother of Emilio Estevez. Martin
Sheen’s child is Ramón Estévez. Joe Estevez’s family name is Estevez. Martin Sheen’s family name is Estévez.
Emilio Estevez is described by the source Obalky knih.cz. Ramón Estévez is a television actor. Emilio Estevez is
a film director. Emilio Estevez is a citizen of the United States of America. Emilio Estevez is a writer. Estévez is
said to be the same as Estevez. Emilio Estevez has a sibling named Renée Estévez. Emilio Estevez was born in
New York City. Ramón Estévez is an actor. Charlie Sheen’s family name is Estévez.
EFSUM Emilio Estevez’s father is Martin Sheen. He was born in New York City in 1962 and is known for his
work as an actor, film director, and writer. He has siblings named Ramón Estévez, Joe Estevez, Renée Estévez,
and Charlie Sheen. Martin Sheen is the father of both Emilio Estevez and Ramón Estévez.
Table 6: An example of verbalized facts for Mintaka.
Evidence-Focused Summarization Prompt (EFSUMprompt)
[Task Description]
You are a knowledge graph summarizer for Question Answering. I will give you “Question”, “Fact triples”. You
should turn triples into *summary*. The *summary* should serve as a context to facilitate QA (Question and
Answer) tasks.
## Caution1: The *summary* should not explicitly mention what the correct answer is.
## Caution2: The *summary* should only contain information of the given triples.
## Caution3: Each triplet is seperated with “\n” and head, relation, tail are provided in head | relation | tail
format.
## Question: {}
## Fact triples: {}
## Summary:
Table 7: The prompt for evidence-focused summarization.
KG-Augmented Question Answering Prompt (KAPING)
[Task Description]
You are a student who have to solve the question. I’ll give you a triples as a context. But if it is not useful, just
ignore it and generate your own guess.
## Triples: {}
## Question: {}
## You are aware of the answer. Generate only short answer(You have to guess something):
KG-Augmented Question Answering Prompt (EFSUM)
[Task Description]
You are a student who have to solve the question. I’ll give you a summary as a context. But if it is not useful,
just ignore it and generate your own guess.
## Summary: {}
## Question: {}
## You are aware of the answer. Generate only short answer(You have to guess something):
Table 8: The prompts for knowledge-augmented zero-shot question answering.
Summary Candidate Paraphrasing Prompt (In case that answer does not exist in the summary candidate)
[Task Description]
You are a knowledge graph summarizer for Question Answering. I will give you “Question”, “Knowledge
Summary”. You should pharaphrase the original “Knowledge Summary”. The paraphrased summary should
serve as a context to facilitate QA (Question and Answer) tasks. Paraphrase the original “Knowledge Summary”
to be more helpful to solve the QA.
## Question: {}
## Original Summary: {}
## Paraphrased Summary:
Summary Candidate Paraphrasing Prompt (In case that answer does exist in the summary candidate)
[Task Description]
You are a knowledge graph summarizer for Question Answering. I will give you “Question”, “Answer”,
“Knowledge Summary”. You should pharaphrase the original “Knowledge Summary”. The paraphrased summary
should serve as a context to facilitate QA (Question and Answer) tasks. Paraphrase the original “Knowledge
Summary” to be more helpful to solve the QA.
## Question: {}
## Answer: {}
## Original Summary: {}
## Paraphrased Summary:
Table 9: The prompt for summary candidate paraphrasing.
Summary Candidate Faithfulness Evaluation Prompt
[Task Description]
You will be given one summary written to provide useful contexts by given source triples from knowledge graphs.
Your task is to check whether the given summary induces factual inconsistency. Please make sure you read and
understand these instructions carefully. Please keep this evaluation creteria open while reviewing, and refer to it
as needed.
Evaluation Criteria:
Factual Inconsistency (0 or 1): Does the summary untruthful or misleading facts that are not supported by the
source triples? If does, mark 1. Otherwise, mark 0.
Evaluation Steps:
1. read and understand the source triples first. note the entities that are in focus and the relations between them.
2. proceed to read through the summary provided.
3. compare the information in the summary with that in the source triples. pay particular attention to the entities,
actions, and relations.
4. mark “1” if the summary contains factual inconsistencies, i.e., if it states untruthful or misleading facts that
are not supported by the source triples.
5. mark “0” if the summary is consistent with the source triples and does not misrepresent the facts provided by
the source triples.
Remember, you are not assessing the quality of the writing, but the factual consistency of the summary compared
to the source triples. perfection in grammar or style does not account for factual consistency. conversely, poor
grammar or style does not necessarily mean factual inconsistency. the key lies in the alignment of facts between
the source triples and the summary.
## Source Triples: {}
## Summary: {}
## Does the summary contain factual inconsistency? Answer:
Table 10: The prompt for faithfulness evaluation.