Systematic Assessment of Factual Knowledge in Large Language Models
Authors: LINHAO LUO
Trang Vu
Dinh Phung
Gholamreza Haffari
Venue: EMNLP 2023 Findings (2023)
Keywords: large language models
hallucination
knowledge graph

Abstract: Previous studies have relied on existing question-answering benchmarks to evaluate the knowledge stored in large language models (LLMs). However, this approach has limitations regarding factual knowledge coverage, as it mostly focuses on generic domains which may overlap with the pretraining data. This paper proposes a  framework to systematically assess  the factual knowledge of LLMs by leveraging knowledge graphs (KGs). Our framework automatically generates a set of questions and expected answers from the facts stored in a given KG, and then evaluates the accuracy of LLMs in answering these questions. We systematically evaluate the state-of-the-art LLMs with KGs in generic and specific domains. The experiment shows that ChatGPT is consistently the top performer across all domains. We also find that LLMs performance depends on the instruction finetuning, domain and question complexity and is prone to adversarial context.

Systematic Assessment of Factual Knowledge in Large Language Models
Linhao Luo
Thuy-Trang Vu Dinh Phung Gholamreza Haffari Department of Data Science and AI
Faculty of Information Technology, Monash University, Australia (linhao. luo, trang. vu1, dinh. .hhnn,hhungg gholamreza. haffari J@monash.edu edu heraaaaessh...m.mss.ho.ooo
Abstract
TFQ: The bärth place of Banack Obass is Hiami.
Tile barth place of Banack Obaas is Ranaii rup: the birth place of Barack Ubems is [hask] Choices: New York, нlaмiá Miani, Ah-question: where was Barack Obame. bom??
IFQ:
Previous studies have relied on existing question-answering benchmarks evaluate knowledge stored in large language models (LLMs). However, this approach has limita- tions regarding factual knowledge coverage, as it mostly focuses generic domains which may overlap with the pretraining data. This paper proposes framework to systematically assess the factual knowledge of LLMs by lever- aging knowledge graphs (KGs). Our frame- work automatically generates and expected answers from the facts stored in given KG, and then evaluates the accuracy of LLMs in answering these questions. We sys- tematically evaluate the state-of offthe-art LLMs with KGs generic and specific domains. The experiment shows that ChatGPT is consistently the top performer across all domains. We also find that LLMs performance depends the in- struction finetuning, domain and question com- plexity prone to adversarial context.'
Shrrack drans,
burn in, Naweia)
Question
Goncration
Generative LLM
Predlct::
of questions
Figure 1: ates diverse set of questions evaluate factual knowl- edge in LLMs.
Our proposed assessment framework gener-
2023; Guo et al., 2023). While these bench- marks are valuable for comparison and measuring progress in LLM research, they may not provide sufficient assessment for production. Benchmarks constructed from public datasets can pose infor- mation leakage problems due to overlap with pre- training data. Furthermore, constructing domain- specific benchmarks is costly, requiring domain expertise and adequate knowledge coverage.
1 Introduction
The rise of Large Language Models (LLMs) has greatly improved the capabilities of natural lan- guage processing (NLP). However, one primary concern with these models is the potential for ex- trinsic hallucinations where LLMs generate state- ments that cannot be verified from the source (Levy et al., 2021; Ji et al., 2023). This issue severely impairs the trustworthiness of LLMs and is par- ticularly concerning when relying LLMs for decision-making. Rigorous evaluation is necessary before deploying them in critical applications.
This paper proposes systematic approach to assess factual knowledge in LLMs by generating comprehensive assessment suite from knowledge graphs (KGs) and evaluating the correctness of LLMs' responses. The question generation process is carefully designed to ensure coverage of facts, as well as diversity and validity of the questions (Figure 1). Using this framework, we evaluate mul- tiple models from three LLM families on factual questions derived from four KGs, covering both generic and specialized domains. Specifically, our contributions
One evaluation approach is to use question- answering datasets to assess the language and knowledge capabilities of LLMSs. Recent research has mainly focused evaluation using existing benchmarks (Bommasani et al., 2023; Bang et al.,
We propose novel framework to evaluate factual knowledge in LLMs by systematically generating valid and diverse questions from KGs while also ensuring knowledge coverage.
Cooe and data will be released at RManLuo/ 1mm- fnnffaaceaal
com/
We observe that LLMs may abstain from an-

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swering certain questions, prioritizing preci- sion by avoiding the provision of inaccurate or hallucinated answers. We propose to use the Fl metric to take the abstention into account and ensure fair comparison across models.
in Appendix).
True-false question (TFQ) Given triplet, we create factual questions that ask the LLM to deter- mine whether a given statement is true or false. For example, given the triplet (Barack Obama, born in, Hawaii), we can generate true statement "The birth place of Barack Obama is Hawaii.". For false statement, we randomly replace the object with wrong entity.
We show that LLMS performance depends on several factors such as instruction finetuning, domains, and question complexity. Despite sharing the same parametric knowledge base, models finetuned with different instruction datasets show varying performance levels. In general-domain KGs, LLMs achieve the high- est score, but their performance declines in specialized domains and is worse on questions having wide range of potential answers.
Multiple choice questions (MCQ) The LLM is presented with a list of answer candidates (choices) and is required to select the correct one. The candi- dates consist of the object along with randomly se- lected incorrect entitics. We consider two formats for MCQ: fill-i-tthe-llank (FiB) by replacing the reference object in the true statement with [MASK] token and Wh-question (Aigo et al., 2021).
We assess robustness of LLMs to the prompt- ing context and find they are highly sensitive to irreeevnt information and susceptible to being misled by antifactual contexts.
Short-answer questions (SAQ) Instead of pro- viding answer candidates as in MCQ, we ask the LLM to predict the correct answer directly in SAQ. For many-to-man relations, we consider all possi- ble objects as potential correct answers and request the LLMs to list all possible answers.
2 Systematic Assessment Framework
This section describes the question generation com- ponent in our proposed assessment framework, fol- lowed by the answer prompting strategy collect LLM's response and the evaluation metric.
2.2 Evaluation
Answer Prompting We carefully design prompts to describe the task and instruct the LLMs to provide concise answers. We also verify the robustness and consistency of LLMs by injecting different types of knowledge into the question, including (i) relevant knowledge, (ii) irrelevant knowledge which correct but not related to the question, and (iii) anti-factual knowledge that provides false or erroneous information. The injected knowledge can come from the relation description or extra evidence information, which are available in several knowledge graphs.
2.1 Question Generation
Our framework leverages the facts stored in KG, organized into triplets, i.e., (subject, relation la- bel, object), to automatically generate set of knowledge-based questions and answers satisfying three requirements: (i) validity: questions should have unique or verifiable answers (ii) coverage: questions should cover all explicit facts; and (iii) diversity: questions should vary in format and diffi- culty.
In this paper, we assume the complete KG and generate valid questions by considering the object of given triplet as the reference answer and gener- ating questions with the subject and relation label. To ensure the question coverage and diversity, we utilize all available triplets and employ two ques- tion generation methods from predefined tem- plate (Petroni et al., 2019) or using ChatGPT (Ope- nAI, 2023). We consider three types of questions: true-false question (TFQ), multiple choice ques- tion (MCQ), and short-answer question (SAQ). In addition, each question type can be represented in different formats: true/false question, fill-in-the- bank (FiB) question, and Wh- question (Figure
Metric Although we prompt LLMs to provide brief and concise answers, evaluating the correct- ness of the generated answer is not trivial. A small percentage of generated answers are long and con- tain explanations. Hence, the standard exact match metric used in suitable metric. Instead, we use a fuzzy match met- ric that checks if the generated answer appears in the reference answers and vice versa.
tasks is not a
Many LLMs employ several guardrails avoid providing inaceurate or hallucinated answers which return an abstained answer (c.g., "I am unable to an- swer the questions without more knowledge."), We

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TFQ
MCQ FiB-TPL FiB-GPT3.5
SAQ
Model
TPL. GPT3.5
Wh-GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
76.98
77.43
60.94
63.82
50.63
5.13
2.47
19.57
44.62
LLaMA-7B
1.23
1.46
7.20
0.76
0.27
2.93
3.07
0.15
2.13
Alpaca
65.07
60.65
41.95
40.50
41.68
7.68
6.01
8.42
34.00
Vicuna
52.83
51.84
15.47
18.28
33.84
3.79
4.81
7.83
23.59
T5-XL
23.87
6.79
5.77
3.96
8.23
1.63
1.69
1.33
6,66
FLAN-T5-XL
75.01
79.75
51.59
50.72
51.66
11.42
9.99
42.71
FLAN-Alpaca
54.83
53.08
46.59
47.58
48.59
9.89
8.06
10.93
34.94
FLAN-Vicuna 63.73
63.80
46.80
46.76
48.69
2.74
10.83
35.78
% abstention
0%
25%
50%
75%
100%
Table 1: Precision of LLMs on different types of questions generated from Google-RE: truc-false question (TFQ), multi-choice question (MCQ) and short-answer questions (SAQ). SAQ and MCQ questions can be in fill-in-the-blank (FiB) or Wh-question (Wh) format. TPL and GPT3.5 denote whether the questions are generated by the template and GPT3.5, respectively. The shade of the background color shows the percentage of abstained responses. The best performance of each question type marked in bold.
define precision the accuracy of non-abstained answers
iii) LLaMA family, including LLaMA-7B (Tou- vron al., 2023), Alpaca (Taori et al., 2023) and Vicuna (Chiang al., 2023) which in- (1) struction finetuned from LLaMA-7B backhone; and (iii) T5 family, including T5-XL (Raffel et al., (2) 2020), FLAN-T5 XL (Chung et al., 2022) and two LLAN--5-XL-basse models which are instruc- tion finetuned on Alpaca and Vicuna datasets, de- noted as FLAN-Alpaca (Chia et al., 2023) and FLAN-Vicuna respectively, The details regarding prompting be found in Appendix A.2.
correct
P correct + incorreel
pa = P (1-A)
and recall as the percentage of accuracy of all ques- tions
correct
R
correct + incorrect + abstained
The Fl score F1 = 2 PxR is the main evaluation P+R
Experiment Settings We employ two question generation methods: (i) template-basee (TPL)
metric to compare the performance of LLMs.
where the subject is plugged into the provided tem- plate and the object the ground-truth answer; and (ii) LLM-based where we use GPT-3. 5-turbo to generate the questions. The question generation prompt can be found in Appendix C. Given triplet, generate the TFQ with the ratio of true and false questions set to 1 1. For MCQ, we randomly lect three incorrect entities and combine them with the correct entities as the choices.
3 Experiments
3.1 Setup
Datasets We use four KGs in LAMA (Petroni et al., 2019) and BioLAMA (Sung et al., 2021) benchmarks generate factual questions, includ- ing two general-domain KGs: Google-RE (Petroni et al., 2019), T-REx (Elsahar et al., 2018), and two domain-specifit KGs: WikiBio (Sung et al., 2021) in biology domain and ULMS (Bodenreider, 2004) in the medical domain. Each relation in the KGs is associated with predefined template to construct natural sentence from given triplet. Detail descrip- tions of the datasets and the predefined templates are reported in Appendix A.I.
3.2 Results
Precision We report the precision of LLMs on question generated from Google-RE in Table 1. As expected, LLMs perform best on TFQ and worst on SAQ due to the increasing difficulty level. Sur- prisingly, almost all LLMs struggle with FiB ques- tions, often returning abstentions the [MASK] token without making any predictions. While FiB questions are commonly used in masked lan- guage model evaluation, we find that Wh-questions, which are more natural and occur more frequently
Large Language Models
We evaluate the knowl- edge captured in several LLMs coming from three backbones: (i) ChatGPT² (OpenAl, 2023);
We did not assess GPT4 due to its high cost.

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I none
Irrekvance
Inivelevance
antifaccuua
TFQ-GPT3.
MCQ WP-G5
SAQ-GPT335
100
100
100
75
0m
ChaGPT
Alpaca
Vicuna
Pba-A
Flan-V
ChanGPT Alpoca
Vicuna
Fla-A
Fba-VV
CharGPT
Alpoca
Vicuma
Fan-A
Faa-V
Figure 2: score of LLMs on Google-RE with different context prompt: none, relevant, irrelevant and antifactual context (best seen in color). FLAN-A and FLAN-B denote FLAN-Alpaca and FLAN-Vi cuna respectively.
in the instruction set, more suitable for evaluat- ing conversational LLMs. Moreover, observe comparable performance between template-basel and GPT3.5-based questions.
Google-RE
TREx
WikiBio
UMLS
ChatGPT
35.77
74.00
62.74
48.99
LLaMA-7B
2.07
8.49
1.26
1.35
Alpaca
32.56
61.00
41.99
36.84
Overall, ChatGPT achieves the best average pre- cision. However, it also has high percentage of abstained answers across all question types. Both the LLaMA-7B and T5-XL models perform worse than random guessing in TFQ and MCQ, indicat- ing a failure follow instructions due to the lack training on instruction finetuning datasets. Al- though sharing the same parametric knowledge base (LLaMA-7B). Alpaca consistently outperforms Vicuna. On the other hand, further instruction fine- tuning the FLAN-T5-XI does not improve precision.
Vicuna
22.86
41.08
25.78
22.99
T5-XL
6.65
11.31
9.51
15.35
FLAN-T5-XL
30.62
57.14
35.82
30.33
FLAN-Alpaca
34.89
58,41
36.13
35.39
FLAN-Vicuna
32.69
54.60
36.60
34.91
Table 2: Average Fl score of LLMS across question types on different KGs. The best score is in bold.
speculate that this discrepancy may be attributed to the complexity of the answer range of the Google- RE questions such date-of-birth, birth place, and death place which have a wide answer range.
F1 Measure Table 2 shows the average Fl score across all question types for each KG. The detailed breakdown of Fl scores for each question type can be found in Appendix B. Overall, ChatGPT out- performs other LLMs, and models from the T5 family generally perform better than those from the LLaMA family. Among the models from the same family, those fine-tuned on the Alpaca instruction set have better performance. This contrasts with the above observation where FLAN-T5-XL XL is the top performer in terms of precision in the T5 family. With the high abstention rate, it can be seen that FLAN-T5-XL XL tends to abstain from uncertain ques- tions to achieve higher precision, which comes the expense of losing recall for correct answers.
Robustness to Adversarial Context We inject different contexts to the questions of Google-RE evaluation set and reported the results in Figure 2. Our observations reveal that the responses of LLMs are highly sensitive to the contexts. Incorporating relevant context leads to significant performance improvement across all LLMs. Conversely, LLMs are prone be misled by antifactual context, de- spite explicitly instructed to base their answers on real-world facts. LLMs performance also decrease when conditioned on irrelevant contexts. These findings highlight the lack of robustness in LLMs against adversarial examples. Ideally, robust LLM should perform comparable in the absence of context with irrelevant context. This poses challenge in deploying LLMs production, they may inadvertently reinforce misinformation provided by users.
As shown in Table 2, the F1 scores on TREx (general domain) are higher than those in specific domains (WikiBio and UMI.S). Additionally, the relatively stronger performance WikiBio over UMLS can be attributed to the pretraining data overlap as is derived from Wikipedia. Interestingly, all LLMSs perform poorly on the Google-RE dataset, despite also being ex- tracted from the general domain (Wikipedia). We
Impact of Domain
Related Works
LLM Evaluation Evaluation of the Large Lan- guage Model (LLM) has gained increasing interest among researchers (Bommasani et al., 2023; Bang

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al., 2023; Guo al., 2023). For instance, Bang al. (2023) conducts multitask, multilingual, and multimodal evaluation for ChatGPT. Holistic Eval- uation of Language Models (HELM) (Bommasani et al., 2023) selects a broad of datasets and bench- marks to evaluate the ability of LLMs. However, previous works mostly focus on human evaluation and using existing datasets and benchmarks (Guo al., 2023). This requires lots of human effort and cannot guarantee the knowledge coverage assess knowledge LLMs comprehensively.
(Seyler al., 2017; Kumar et al., 2019; Indurthi et al., 2017: Chen al., 2023). Recently, many methods harness the ability of LLMs to generate questions from KGs (Guo et al., 2022; Axelsson and Skantze, 2023). In this way, they can generate questions with different diversities and complexi- ties. Although there are previous works that gener- ale questions from knowledge graphs, the best of our knowledge, none of them adopt the generated questions for evaluating the factual knowledge LLMs.
Factual Knowledge Evaluation for LLMs Eval- uating the factual knowledge of LLMs can ensure the model is providing reliable and trustworthy information to users. Knowledge Graphs (KGs), which capture vast amounts of facts, offer reli- able source of factual knowledge for evaluation (Pan et al., 2023; Luo et al., 2023). LAMA (Petroni al., 2019) adopts pre-defined templates to con- vert the facts in KGs into cloze questions then uses LLMs to predict the answers. The prediction re- sults are used to evaluate the knowledge stored in LLMs. Similarly, BioLAMA (Sung et al., 2021) and MedLAMA (Meng ct al., 2021) assess the fac- tual knowledge of LLMs in medical domains by us- ing medical knowledge graphs. Alex et al. (Mallen et al., 2022) selects unpopular facts from Wiki- data knowledge graphs which have low-frequency clicked entities investigate the ability of LLMs retain less popular factual knowledge. By enu- merating all available factual triplets in KGs, we could ensure the evaluation coverage of the fac- tual knowledge. Nevertheless, exciting methods lack systematic framework containing question generation and evaluation modules. They often pre-defined templates for question generation which cannot provide diverse questions evaluate the knowledge of instruction-tunne LLMs (Sun et al., 2023).
Conclusion
We propose a systematic framework evaluate factual knowledge of LLMs with the diverse and well-coverage questions generated from KG. The experiment reveals several factors affecting LLMs' performance and highlights their vulnerability to adversarial context. Our findings contribute to un- derstanding LLMs' capabilities and limitation in handling factual knowledge.
Limitations
The limitation of our work includes
Assuming completed knowledge graph. In work, we access the knowledge of LLMs by using the facts in knowledge graphs. How- ever, knowledge graphs are often incomplete, which could contain lots of implicit facts. Thus, it could be inadequate to evaluate the LLMs with the existing KGs. In the future, we plan to incorporate the knowledge graph completion methods and present more com- prehensive assessment framework.
Focusing only triplet- facts. We only assess the knowledge of LLMs by using the question generated from the single triplet, which ignores the complex knowledge rep- resented by the combination of triplcts. To assess the completed knowledge, we need to design a framework that considers the reason- ing ability of LLMs on knowledge graphs.
Automatically Question Generation from KGs To assess knowledge in instruction-tuning LLMs, we need to evaluate whether they have such knowl- edge and whether they can accurately express their knowledge, i.e. instruct following ability and ro- bustness. Therefore, given the same factual knowl- edge, we need to generate diverse questions at dif- ferent levels of difficulty. Early works that gen- erate questions from KGs either use sequence-to- sequence models or graph neural networks to con- vert the triplet into a natural language question
Evaluating the correctness of multiple answer questions. For N-M relations, have multi- ple answers a question. However, the LLMs might not return all the answers. How to eval- uate the partially answered questions is still an open question for accessing the knowledge of LLMs.

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Ethics Statement
evaluation of instruction tuned large language mod- els. arXiv preprint arXiv:2306. 04757.
Our work aims to design a framework that can au- tomatically assess the factual knowledge stored in large language models. In this research, we con- ducted experiments on publicly available datasets and implemented our approaches using commonly accepted techniques, giving utmost consideration to fairness and avoiding potential biases. We ac- knowledge the significance of transparency and have furnished comprehensive elucidations regard- ing our methodology and decision-makin process. To conclude, our research adheres to ethical guide- lines and poses no potential risks.
Wei- Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang. Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, lon Stoica, and Eric P. Xing. 2023. Vicuna: An open- source chatbot impressing gpt-4 with 90%* chatgpt quality.
Hyung Won Chung. Le Hou, Shayne Longpre, Bar- ret Zoph, Yi Tay. William Fedus, Eric Li, Xuezhi Wang. Mostafa Dehghani, Siddhartha Brahma, et al. 2022. Scaling cestucciionfinetundd language models. arXiv preprint arxi::2210.11411
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.
A Acknowledgments
This research is supported by the ARC Future Fel- lowship FT1901 00039. The authors are grateful the anonymous reviewers for their helpful com- ments.
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023. How close is chatgpt human experts? comparison corpus, evaluation, and detection. arXiv preprint arXiv:230.005997.
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KGs
Domain
#Relations
#Entities
#Triplet
L.M
#params
Model Implementation
Google-RU RE
General
7,242
55,28
ChatGPT
GPT-3.5 3.5-turbo
T-Rex
General
46
31,180
34,039
LLaMA-7B
7B
Touvron et al. (2023)
WikiBio
Biology
5
68,39
17,582
Alpaca
7B
Taori et al. (2023)
ULMS
Medical
17
18,910
64,305
Vicuna
7B
Chiang et al. (2023)
3B
Table 3: Dataset Statistics
FLAN-TS-XL
3B
google/flan-t5-
FLAN-Alpaca
3B
declaare-lab/llnn--llaaa---
FLAN-Vicuna
3B
Lmsys/faascchat-t6-8bv....
A Implementation and Experiment Detail
Table 4: Large language model (LLM) description and
A.1 Dataset
statistics.
We evaluate LLMs with the knowledge derived from the following KGs
Triplet: (Barack bom Hawali)
Question Generation Methods
T-REx (Elsahar al., 2018), knowledge graph extracted from Wikipedia. T-REx in- cludes a relation label, a description, and template (Table 20) for each relation which can be used to generate cloze sentences.
Template based
ChatGPT-based
True-faise questions (TFO)
Prompt: Please predict whethhr this fact is correct or not?
False statement: The birth place of Barack Obama Miami. True stacment: The
Barack Obama Hawall.
Multipla choice quasiiios (MCQ)
Google-RE (Petroni et al., 2019) is subset of knowledge graphs containing three relations: place of birth, date of birth, and place of death. The fact triplets associated with each relation are extracted from Wikipedia and aligned with short piece of support text. Table 19 shows the predefined template for each relation in Google-RE.
Prompt: Please select the
the given cholces.
Choices: New York, Miam, Hawai....
Fill-i-bllan:: The birth of Barack Obama [MASK]
Wh- question: Where was Barack Obama bom??
Short-answer questions (SAQ)
Prompt:
following
Fill-in-blank: The birth place Barack Obama is [MASK],
Wh- question: Where was Barack Obama bom??
Our question generation process iterates through all fact triplets and creates multiple question types for each triplet.
Figure 3:
Wikipedia Biography Dataset (WikiBio) (Sung et al., 2021) is a biology knowledge graph that constructed by extracting the biology-related facts from Wikidata. Table 21 shows the template for each relation in Wik- iBio.
Question Generation Given a triplet, we gener- ate the TFQ with the ratio of true and false ques- tions set to 1 1. For MCQ, we randomly select three incorrect entities and combine them with the correct entities as the choices. Table 5 shows the number of generated questions for each KG, We also illustrate the example template-base based and LLM-based questions Figure
Unified Language Medical System (ULMS) (Bodenreider, 2004) is medical knowledge graph constructed by domain experts. It con- tains information about various medical con- cepts and their relationships. Table 22 shows the template for each relation in UMLS.
bstained Answer Detection Assessing the ac- curacy of answers generated by LLMs in free text format presents a challenge in determining both the correctness of the answer and whether the model chooses to abstain from answering. For TFQ, instead of treating it binary classification problem, we instruct the model respond with "UNKNOWN" when uncertain, effectively trans- forming into a 3-class text classification task. For MCQ and ASQ, we compile curated list phrases that indicate abstention, such as "cannot
Table 3 reports the domains and data statistics.
A.2 Implementations
Large Language Model Face implementation of LLaMA and the T5 family, The inference process is run on a single RTX8000 GPU with 48GB memory with fixed-precisson (FP16).
We the Hugging-

---

Question
Google-RE
TREx
WikiBio
UMLS
G_RE
TREx
WikiBio
UMLS
TFQ
11,056
68,078
35,164
128,610
Wh- FiB TFQ
TPL
95.18
98.37
99.97
99.70
MCQ
5,528
34,039
17,582
64,305
SAQ
5,506
32,454
7,391
35,958
TPL
74.12
79.33
87.53
87.53
Table 5: Number of generated questions for each ques- tion type: true-false question (TFQ), multi-choice ques- tion (MCQ), short-answer question (SAQ).
GPT3.5
50.39
64.97
81.78
78.98
Table 7: Diversity measure (self-bleu score) of template- based and LLM--ased questions.
G_RE
TREx
WikiBio
UMLS
TFQ
90.79
92.14
90.75
90.07
in Table 7, we can see that compared to the TFQ and FiB questions generated based on templates. The Wh- questions generated by ChatGPT achieve higher diversity, which provides a more natural and clear instruction to assess the knowledge.
FiB
92.11
92.78
91.86
89.39
Table 6: Similarity (BERT score) between template- based and LLM-based questions, w.r. two question for- mats: true/false question (TFQ) and fill-in-blank (FiB).
F1 score Fl score on different question types are shown in Tables to 11.
predict" or am sorry," and check any of these phrases appear in the output. If such phrases are de- tected, we consider the answer to be an abstained.
Precision Precision score different KGs are shown in Tables 12 to 14. Similar to Google-RE, ChatGPT are the top performer across all KG, fol- lowed by FLAN-T5-XL.
Answer Prompting Each relation in Google-Ro KG comes with the corresponding paragraphs from which it is extracted. We treat this paragraph as relevant context for given triplet. We sample the paragraph from an unrelated triplet, i.c. not sharing subjects or objects as irrelevant context. For the antifactual context, we replace the correct answer with randomly selected entity from KG.
Recall score on different KGs are shown in Tables 15 to 18.
Recall
Adversarial Context The Fl score of different LLMs under different types of context is shown in Figure
B Additional Results
C Example Prompts
Question Analysis We first evaluate the valid- ity of the LLM-based questions by calculating the similarity between the template-based questions in Table 6. Then, we report the diversity of LLM- based questions in Table 7. Since the templates are written by humans, higher similarities indicate the higher validity of the LLM-based question. From the results in Table 6, we can find that LLM- based questions are highly similar template-basee ques- tions across all datasets w.r.t two question formats: true/false question (TFQ) and fill-in-blank (FiB), This verifies the good quality of the LLM-based questions which can be further used to assess the factual knowledge of LLMs.
Question Generation Prompt The question gen- eration prompts for TFQ, FiB and Wh- question can be found in Table 23, Table 24 and Table 25 respectively.
Answer Prompts Table 26 provides the prompt template for different LLM families. LLaMA-7B and models finefuned on Alpaca dataset are prompted with the same instruction format. On the other hand, Vicuna and FLAN-T5-XL employ different templates. The instructions also vary for different question types and formats, as shown in Table 27.
Although the accountability of the template, the task of defining diverse templates can be quite bur- densome. Due to the lack of templates for Wh- questions, we evaluate the diversity of Wh- ques- tions generated by ChatGPT using the self-bleu scores (Zhu et al., 2018). The lower the scores, the more diverse the questions. From the results

---

TFQ
MCQ
SAQ
Model
TPL GPT3.5
FiB-TPL F
Wh-GPT3.5
FiB-TPL FiB-G T Wh-GPT3.5
AVG
ChatGPT
65.06
67.23
47.25
53.20
46.36
2.22
1.21
3.63
35.77
LLaMA-7B
1.11
1.10
7.20
0.76
0.27
2.92
3.07
0.15
2.07
Alpaca
60.99
53.66
41.95
40.50
41.68
7.30
6.01
8.39
32.56
Vicuna
51,51
50.57
15.17
18.09
33.53
3.34
4.77
5.90
22.86
T5-XL
23.85
6.76
5.77
3,96
8,23
1.63
1.69
1.33
6.65
FLAN-T5-XL
31.82
26.15
51.59
50.72
51.66
11.57
11.42
9.99
30.62
FLaca
54.82
53.06
46,59
47.58
48.59
9.47
8.06
10.93
34.89
FLAN-Vicuna
52.92
49.91
46.80
46.76
48.67
2.89
10.83
32.69
Table 8: F1 on different question types generated from Google RE KGs.
TFQ TPL
MCQ
SAQ
Model
GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
85.43
82.38
90.32
87.17
87.61
49.17
52.57
57.38
74.00
LLaMA-7B
2.10
3.63
2.69
3.82
4.36
31.73
19.42
0.19
8.49
Alpaca
66.95
67.77
67.05
65.77
73.50
45.36
44.32
57.27
61.00
Vicuna
56.52
55.87
29.62
27.46
46.74
34.54
29.27
48.58
41.08
T5-XL
20.79
21.99
7.23
4.99
12.75
6.70
1.52
14.47
11.31
FLAN-T5-XL
66.88
58.98
77.46
73.80
78.87
36.14
26.78
38.22
57.14
PLac
69.75
62.45
72.71
70.44
75.39
37.33
30.96
58.41
FLAN-Vicuna
70.43
64.65
74.71
70.75
76.42
19.41
18.42
41.99
54.60
Table 9: different question types generated TREx KGs.
TFQ TPL GPT3.5
MCQ
SAQ
Model
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
71.27
69.07
81.77
86.57
79.07
36.45
38.72
39.02
62.74
LLaMA-7B
3.49
0.36
0.38
2.78
1.43
1.26
Alpaca
59.28
58.75
38.77
$7.24
45.35
7.41
34.74
34.40
41.99
Vicuna
51.15
52.55
13.02
15.45
13.54
21.20
34.90
25.78
T5-XL
16.85
29.05
5.65
6.46
5.17
0.96
9.07
9.51
TLAN-T5-XL XL
35,43
50.36
44.23
63.17
52.57
10.44
14.09
16.30
35.82
FLAN-Alpaca
49.75
51.73
25.44
58.68
44.46
15.15
22.16
21.67
36.13
FLAN-Vicuna
55.21
54.79
45.89
63.06
49.72
3.62
3.02
17.45
36.60
Table 10: F1 different question types generated from WikiBio KGs.
TFQ TPL. GPT3.5
MCQ
SAQ
Model
FiB-TPL FiB-GPT3.5
Wh-GPT3.5
FiB-TPL. FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
68.12
54.67
70.78
83.52
63.11
21.43
18.05
12.21
48.99
LLaMA-7B
0.84
1.73
0.47
0.30
1.28
3.04
3.12
0.00
1.35
Alpaca
55.21
49.60
33.66
63.32
35.87
12.71
36.55
7.80
36.84
Vicuna
51.36
50.00
12.16
21.06
11.72
7.79
19.56
10.26
22.99
TS-XL
32.29
30.81
5.87
9.30
6.95
1.57
34.34
1.69
15.35
FLAN-T5-XL
23,80
34.12
41.41
69.90
45.69
12.45
8.89
6.40
30.33
FLAN-Alpaca
50.81
50.31
34.63
63.48
42.47
8.91
26.70
5.82
35.39
FLAN-Vicuna
51.54
51.51
46.46
72.98
45.99
3.73
1.50
5.53
34.91
Table 11: Fl on different question types generated from UMLS KG.

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TFQ
MCQ
ASQ
Model
TPL
GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
88.63
85.10
91.40
88.13
88.11
57.38
58.36
64.12
77.65
LLaMA-7B
3.22
5.17
2.69
3.82
4.36
32.21
19.43
0.19
8.89
Alpaca
71.89
71.52
67.05
65.80
73.50
45.75
44.39
57.31
62.15
Vicuna
57.85
57.18
29.83
27.71
46.97
34.77
29.48
51.02
41.85
TS-XL
20.81
22.24
7.23
4.99
12.75
6.70
1.53
14.47
11.34
Flan-T5-XL 85.89
79.69
77.46
73.80
78.87
36.64
27.49
38,22
62.26
Flan-Alpaca 70,30
63.18
72.71
70.47
75.39
37.33
30.98
48.22
58.57
Flan-Vicuna
77.45
69.81
74.71
70.76
76.42
19.68
18.43
42.01
56.16
% invalid responses
0%
25%
50%
75%
100%
Table 12: Precision on different question types of invalid responses.
TREx KGs. The shade of background color shows the percentage
TFQ TPL GPT3.5
MCQ
SAQ
Model
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
75.75
72.29
81.83
86.65
79.09
39,54
40.4
41.27
64.60
LLaMA-7B
0.34
4.03
0.36
0.338
2.78
1.49
1.43
0.08
1.36
Alpaca
62.08
60.33
38.77
57.24
45.35
7.94
34.74
34.58
42.63
Vicuna
52.5
53.72
13.12
15.57
13.57
4.5
21.26
35.44
26.21
17.13
29.2
5.65
6.46
5.17
0.96
9.07
2.87
9.56
FLAN-T5
74.01
61.07
44.23
63.17
$2.57
10.44
14.61
16.3
42.05
FLAN-Alpaca
52.11
51.91
25.44
58.68
44.47
15.15
22.16
21.67
36.45
FLAN-Vicuna
59.65
55.38
45.89
63.06
49.72
5.18
3.91
17.45
37.53
%6 abstention
0%
25%
50%
75%
100%
Table 13: Precision different question types generated from wikibio KG. The shade of background color shows the percentage of abstained responses.
TFQ
MCQ
SAQ
Model
TPL GPT3.5
FiB-TPL FiB-GPT3.5
Wh-GPT3.5
FiB-TPL
FiB FiB-GPT3.5
Wh-GPT3.5
AVG
ChatGPT
73.20
59.09
71.09
83.60
64.04
22.76
18.43
14.39
50.83
LLaMA-7B
0.94
1.96
0.47
0.30
1.28
3.04
3.12
0.00
1.39
Alpaca
57.71
53.54
33.67
63.34
35.88
12.72
36.58
7.86
37.66
Vicuna
52.52
51.07
12.29
21.23
11.78
7.84
19.62
10.51
23.36
TS-XL
32.32
31.40
5.87
9.31
6.95
1.57
34.36
1.69
15.43
F1an-T5-XL
69.66
55.00
41.44
69.94
45.73
12.45
8.89
6.40
38.69
Flan-Alpaca
54.63
51.56
34.67
63.53
42.52
8,91
26.72
5.82
36.05
Flan-Vicuna
62.52
53.10
46.49
73.01
46.02
3.73
1.50
5.53
36.49
% abstention
0%
25%
50%
75%
100%
Table 14: Precision on different question types generated from UMLS KG. The shade of background color shows the percentage abstained responses.

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TFQ
MCQ
SAQ
Model
TPL GPT3.5
FiB-TPL F
Wh-GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
56.33
59.41
38.59
45.60
42.75
1,42
0.80
2.00
30.86
LLaMA-7B
1.01
0.89
7.20
0.76
0.27
2.92
3.07
0.15
2.03
Alpaca
57.38
48.11
41.95
40.50
41.68
6.96
6.01
8.35
31.37
Vicuna
50.26
49.36
14.87
17.91
33.21
2.98
4.74
4.74
22.26
T5-XL
23.83
6.74
5.77
3,96
8,23
1,63
1.69
1.33
6.65
FLAN-T5-XL
20.20
15.64
$1.59
50.72
51.66
11.57
11.42
27.85
FLAN-Alpaca
54.82
53.04
46,58
47.58
48.59
9.08
8.06
10.93
34.84
FLAN-Vicuna
45.24
40.99
46.80
46.76
48.64
2.89
10.82
30.61
Table 15: Recall on different question types generated from Google RE KGs.
TFQ TPL
MCQ
SAQ
Model
GPT3.5
FiB-TPL FiB-GPT3.5
Wh-GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
82.46
79.84
89.27
86.23
87.12
43.02
47.83
51.93
70.96
LLaMA-7B
1.56
2.8
2.69
3.82
4.36
31.27
19.4
0.19
8.26
Alpaca
62.63
64.4
67.04
65.74
73.5
44.97
44.25
57.23
59.97
Vicuna
55.25
54.61
29.42
27.21
46.5
34.31
29.06
46.37
40.34
T5-XL
20.78
21.76
7.23
4.99
12.75
6.7
1.52
14.47
11.28
FLAN-T5-XL
54.76
46.81
77.46
73.8
78.87
35.66
26.1
38.22
53.96
FLaN-Alpaca
69.2
61.74
72.71
70.4
75.39
37.33
30.93
48.22 48.22
58.24
FLAN-Vicuna
64.58
60.2
74.71
70.74
76.42
19.14
18.42
41.97
53.27
Table 16: Recall different question types generated TREx KGs.
TFQ TPL GPT3.5
MCQ
SAQ
Model
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
FiB-TPL FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
67.28
66.13
81.72
86.49
79.04
33.82
37.18
37.00
61.08
LLaMA-7B
3.07
0.36
0.38
1.43
1.19
Alpaca
56.72
57.25
38.77
$7.24
45.35
6.95
34.74
34.21
41.40
Vicuna
49.88
51.42
12.92
15.33
13.51
21.15
34.39
25.37
T5-XL
28.89
5.65
6,46
5.17
0.96
2.87
9.46
FLAN-T5-XL XL
23.29
42.84
44.23
63.17
52.57
10.44
13.61
16.30
33.31
FLAN-Alpaca
47.59
51.56
25.44
58.68
44.45
15.15
22.16
21.67
35.84
FLAN-Vicuna
51.38
54.21
45,89
63.06
49.72
2.78
2.46
17.45
35.87
Table 17: Recall different question types generated from wikibio KGs.
TFQ TPL. GPT3.5
MCQ
SAQ
Model
FiB-TPL FiB-GPT3.5
Wh-GPT3.5
FiB-TPL. FiB-GPT3.5 Wh-GPT3.5
AVG
ChatGPT
63.70
50.86
70.48
83.43
62.21
20.24
17.70
10.60
47.40
LLaMA-7B
0.75
1.55
0.47
0.30
1.28
3.04
3.12
0.00
24.36
Alpaca
52.91
46.19
33.65
63,30
35.85
12.71
36.52
7.74
18.71
Vicuna
50.24
48.98
12.04
20.90
11.37
7.75
19.50
10.02
29.35
TS-XL
29.29
28.36
5.86
9.29
6.94
1.57
34.31
1.69
18.63
FLAN-T5-XL
14.35
24.73
41.38
69.87
45.65
12.45
8.89
6.40
30.13
FLAN-Alpaca
47.49
49.11
34.59
63.43
42.43
8.91
26.67
5.82
31.39
FLAN-Vicuna
43.84
50.00
46.44
72.94
45.95
3.73
1.50
5.53
34.27
Table 18: Recall on different question types generated from UMLS KGs.

---

Relation
Type
Template
date_of. birth
N-1
The birth date of [X] is [Y].
place_ of birth
N-1
The birth place of [X] is [Y].
place_of_dest _e_tt
N-1
The death place of [X] is [Y].
Table 19: Examples of question generation template for Google_Ri RE, where [X] denotes the subject, and [Y] denotes the object.
Relation
Type
Template
capital
1-1
The capital of [X] is [Y].
member of political party
N-1
[X] is member of the [Y] political party,
shares border with
N-M
[X] shares border with [Y].
Table 20: Examples of question generation template for Trex, where [X] denotes the subject, and [Y] denotes the object.
Relation
Type
Template
drug used for treatment medical condition treated
N-M
The standard treatment for patients with [X] a drug such as [Y].
N-M
[X] has effects on diseases such as [Y].
therapeutic area
N-M
[X] cures diseases such as [Y].
Table Examples question generation template for WikiBio, where [X] denotes the subject, and [Y] denotes the object.
Relation
Type
Template
may_be_ sr__be_preventedd by disease
N-M
[X] treats [Y].
gene gene_mapped mapped_ppppaaaoeeee Beaeddd______sssid
N-M
[X] has genetic association with [Y].
may_ be _ea__eefinding__ of discase
N-M
[X] has symptoms such as [Y].
Table 22: Examples of question generation template for UMLS, where [X] denotes the subject, and [Y] denotes the object.
TRUE-FALSE QUESTION
I have a triplet extracted from a knowledge graph. The triplct is organized as (Subject, Relation, Object), which describes the relation between object and relation. Can you help me to generate a natural language sentence to describe this triplet accurate as possible?
( (riplett)
Table 23: Question generation prompts for true-false question format.
FILLL-IN-BLANK QUESTION
I have triplet extracted from a knowledge graph. The triplet is organized (Subject, Relation, Object), which describes the relation between object and relation. Can you help to generate natural language sentence describe this triplet accurate possible and replace Object with [MASK]]
( triipet /
Table 24: Question generation prompt for fill-in-blan question format.

---

WH- QUESTION
have a triplet extracted from a knowledge graph. The triplet is organized as (Subject, Relation, Object), which describes the relation between object and relation. Can you help me to generate a question based on this triplet that the object the corresponding answer? Please return the question only.
| triplet)
Table 25: Question generation prompt for Wh- question format.
ChatGPT
context ]
intructions }
[ question ]
LLaMA-7B, Alpaca, FLAN-Alpaca
Below an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
[ context )
intructions } ### Input:
questiinn } ### Response:
Vicuna, FLAN-Vicuna
context ]
intructions )
HUMAN:
quesstion /
ASSISTANT:
F--XL.FFaa--5--
( context }
(intructions }
QUESTION: / question
Table 26: Inference prompt format for different LLMs.

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mone
Brelevancee
Biieelevance
I antiffacuual
100
TFQ Template
100
TFQ GPT3.5
ChartPT LLaMA
Alpaco
Vicuna
15
1aa-115
Hun A Han V
ChariFT
Alpaco
Vicusa
15
Flm--15
Hlnn A
Hln V
1-00-FiB-Templaee
INC0-OP335-Tomptee
22
30
50
L
ChiGPT 11aMA Alpaci
Visuma
TS
Fon-TS Pla-A
PlanVV
11aMA Alpasi
Viaum
13
Ran-TS
PlanAA
Pbn-V
100
MCQ-Q-GPT3..
100
M
CluiGIT
Alpaco
Fla-TS
Rla-V
LLMA Abjaca
Faa-TS
Fla-A
Fln-VV
100
SAQ-FiB GPT3.5
100
SAQQQ:GPT335
50
50
m
11
1
1m
11
ChaiUPT LLMMA
Alpaca
Vicuna
13
Flaz 15
Plan A
Flan V
CharGPt LLMMA
Alpaca
Vizuna
15
15a
F1an
A
Flat
V
Figure 4: score LLM on google_re re with different context prompt: no context (none), relevant, irrelevant and antifactual context.
TFQ
The following sentence describes a real-world fact. Please predict whether this fact is correct or not? Please only return correct or incorrect. If don't know, please answer UNKNOWN, If correct, answer CORRECT. If incorrect, answer INCORRECT.
FiB
Please predict the missing words to complete the following sentence based on the facts in the real-world. The missing words are represented by [MASK]. Please return the missing words only.
MCQ
Please answer the following questions based on the facts in the real-world. Please select the answers from the given choices and return the answer only.
Choices:
{ choices }
SAQ
Please answer the following questions based on the facts in the real-world. Please keep the answer as simple as possible and return the possible answers as a list.
Table 27: Instructions for different question type.


Reviews:
[{"review_id": "WKpfI7wup5", "reviewer": "Reviewer_njva", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We would like to express our gratitude for taking the time to review our paper and the insightful feedback you have provided. We have carefully considered each of your points and have prepared responses to address the concerns you raised.\n\n> **The proposed evaluation pipeline is similar to prior works.**\n\nEvaluation of the Large Language Model (LLM) has gained increasing interest among researchers. Despite prior works attempt to evaluate knowledge in LLMs [1], most lacks a systematic framework for rigorous evaluation and often cannot be applied to recent generative LLMs. Our work, on the other hand, proposes a novel framework for evaluating the factual knowledge of arbitrary LLMs such as ChatGPT and Alpaca.  Our proposed framework has the capability to tackle the absence of domain-specific benchmarks as well as mitigate the leakage issue of public off-the-shelf benchmarks for a more reliable assessment.\n\nTo do so, we  generate three types of questions from Knowledge Graphs (KGs): True/False Questions (TFQ), Multiple Choice Questions (MCQ), and Short Answer Questions (SAQ). These question types allow us to access different levels of knowledge in LLMs while ensuring comprehensive knowledge coverage, i.e. we generate questions for all factual triplets. Additionally, we introduce a new F1 metric that addresses the abstention problem commonly found in advanced LLMs. This metric is more suitable for evaluating LLM performance.\n\nWe appreciate it if the reviewer could additionally guide us on any missing prior works that we may have overlooked.\n\n> **ChatGPT shows a great percentage of abstention than other models. Is that fair to compare their accuracies?**\n\nMany LLMs may return abstention answers to prevent hallucination and misuse. LLMs with higher abstention tend to avoid providing inaccurate or hallucinated answers, which prioritizes the precision metrics, i.e. accuracy. For a fair comparison among different LLMs, we introduce the recall metric in Eq.3, which takes the abstention answer into account. Furthermore, we introduce the F1 score, which balances the precision and recall metrics, providing a general metric for evaluation. The results of F1 of different LLMs can be found in the Appendix.\n\n> **Reproducibility. The author doesn't mention if the code will be available to the public.**\n\nWe thank the reviewer for the interest in our framework. We will release both the code of our framework and the generated test sets from each KG upon acceptance.\n\n[1] Petroni, F., Rocktäschel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A. H., & Riedel, S. (2019). Language models as knowledge bases?. arXiv preprint arXiv:1909.01066.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "**Strengths**\n1. LLM evaluation is a timely problem and worth attention. \n2. This paper conducts a comprehensive analysis which covers various LLMs, question types and knowledge domains.", "reasons_to_reject": "**Weaknesses：**\n1. The proposed evaluation pipeline is similar to prior works.\n2. ChatGPT shows a great percentage of abstention than other models. Is that fair to compare their accuracies?\n3. Reproducibility. The author doesn't mention if the code will be available to the public.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a framework to systematically evaluate the factual knowledge of LLMs with KGs in generic and specific domains.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "xyCeIOAw2C", "reviewer": "Reviewer_HmbN", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for reviewing our paper and providing insightful feedback. We have carefully considered your points and prepared responses to address your concerns.\n\n> **Automatically generating questions from knowledge graphs must have been utilized somewhere else.**\n\nAlthough there are previous works that generate questions from knowledge graphs [1], to the best of our knowledge, none of them adopt the generated questions for evaluating the factual knowledge in LLMs.\n\n> **The difference of this paper and previous works seems less significant.**\n\nThere are two main motivations for our proposed evaluation frameworks.\n- (i) The lack of benchmark datasets for a specific domain: our framework utilizes KG in the domain of interest to generate evaluation benchmarks. By enumerating through all available factual triplets, we ensure the factual knowledge coverage of the generated benchmarks. \n- (ii) Benchmark data contamination where the test set may appear in the pretraining dataset. Due to the large scale of pretraining data, estimating the overlap ratio between pretraining dataset and test set is time-consuming and requires lots of computational resources. Instead of relying on the off-the-shelf benchmarks, we propose to generate a diverse test set containing different types of questions at varying difficulty levels to ensure rigorous evaluation.\n\nSome prior works attempt to evaluate knowledge in LLMs [2]. However, they lack a systematic framework, including diverse question generation with adequate knowledge coverage and evaluation. Furthermore, their methods may not be suitable for evaluating generative LLMs due to the need to finetune a prediction module. Moreover, they often use the accuracy metric and do not take abstention answers into account, which is essential for a fair evaluation.\n\n> **why on earth use the knowledge graphs...?**\n\nThe knowledge graphs contain enormous factual knowledge in a structural format, which is transparent to users. Thus, we try to utilize the KGs to evaluate the knowledge implicitly stored in LLMs. Since the factual knowledge in KGs is structurally represented (subject, relation, object), we can automatically generate the question-answer pair to assess the existence of knowledge in LLMs.\n\n Moreover, there are many domain-specific KGs available, which can be used to evaluate the knowledge of LLMs in specific areas, such as medical domain. In our experiments, we choose two kinds of KGs: (i) general KGs (Trex and Google-Re) and (ii) domain-specific KGs (Wiki-bio and UMLS) to assess both general factual knowledge and domain-specific knowledge. \n\n> **This paper feels more about evaluating LLMs and presenting the evaluation results but rather the effectiveness and innovativeness of their proposed framework**\n\nIn this paper, we provide a systematic framework containing question generation and evaluation modules. This allows users to evaluate the factual knowledge stored in LLMs more effectively, instead of relying on off-the-shelf benchmarks. The experiment results are illustrated to demonstrate the effectiveness of our framework in assessing knowledge in LLMs and present the potential results when using our framework. Our framework could be readily applied to assess the knowledge of LLMs before deployment in high-stake applications such as medical diagnosis.\n\n- [1] Seyler, D., Yahya, M., & Berberich, K. (2017, October). Knowledge questions from knowledge graphs. In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval (pp. 11-18).\n- [2] Petroni, F., Rocktäschel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A. H., & Riedel, S. (2019). Language models as knowledge bases?. arXiv preprint arXiv:1909.01066.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper proposes a method to automatically generate 3 types of questions from knowledge graphs to assess factual knowledge of LLMs, which is an important topic to inspect LLMs on extrinsic hallucinations. \n\nKnowledge graphs representing facts is more easier to obtain than Q&A datasets. This method might saves effort in constructing domain-specific or general datasets used for assessing factual knowledge of models ( or at least provide inspiration?).\n\nOther consideration like the robustness of LLMs and taking abstention into account when evaluating for fairness, makes the work more complete and rigorous.\n", "reasons_to_reject": "The idea of automatically generating questions from knowledge graphs must have been utilized somewhere else, making this work less valuable, since it is just ‘moving’ a method into the context of assessing factual knowledge of LLMs. So, I think this work is more like an empirical study.\n\nThe difference of this paper and previous works seems less significant. The authors kinda blur the discussion on whether their selected datasets has leakage problems, which is one of their motivations. Besides, another motivation of easier dataset-construction is also not sufficiently proved or demonstrated, making me wonder ‘why on earth use the knowledge graphs...?’\n\nThis paper feels more about evaluating LLMs and presenting the evaluation results  but rather the effectiveness and innovativeness of their proposed framework, which makes me confused.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a framework to evaluate factual knowledge in LLMs by generating valid, diverse and widely-covering questions. They leverages facts represented as triplets in Knowledge Graphs to automatically generate: True-False Question, Multiple Choice Question and Short Answer Question. Then use designed prompts to instruct LLMs, while also injecting relevant, irrelevant and anti-factual knowledge in questions to test robustness and consistency. ", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "r7K96nIzaU", "reviewer": "Reviewer_yo9m", "summary": "", "questions": "Please refer to reason to reject.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for reviewing our paper and providing insightful feedback. We have carefully considered your points and prepared responses to address your concerns.\n\n> **Whether the benchmark proposed in the paper can genuinely assess factual knowledge in language models.**\n\nIn our framework, we focus on evaluating the knowledge of instruction tuning LLMs. Assessing knowledge in instruction tuning LLMs can be done from two perspectives:\n- whether they have such knowledge, usually from the pre-training datasets. Additionally, different instruction-tuning datasets can enhance different skills and may lead to catastrophic forgetting of the knowledge learned in the pretraining stage [1].\n- whether they can accurately express their knowledge,i.e. instruct following ability and robustness\nGiven the same factual knowledge, we generate multiple questions at different levels of difficulty, ranging from easy (true-false question) to hard (short answer question) with different contexts. This allows us to rigorously assess whether such knowledge is stored in the LLM.  Alpaca, Vicuna, and LLaMA are pre-trained on the same training datasets, indicating they should have the same factual knowledge. The performance difference indicates the different effect of instruction tuning in learned factual knowledge in the LLaMA backbone. Additionaly, this also show that they have different abilities in expressing their knowledge. \n\n[1] Wang, Y., Ivison, H., Dasigi, P., Hessel, J., Khot, T., Chandu, K. R., ... & Hajishirzi, H. (2023). How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources. arXiv preprint arXiv:2306.04751.\n\n> **why the evaluation method proposed in this paper would not be affected by such overlap**\n\nThe overlap between public benchmarks and pretraining dataset can hinder the reliability of the evaluation results of the LLMs. Ensuring the non-overlap between training and testing dataset is not the goal of our proposed evaluation framework. We aim to achieve a reliable and rigorous evaluation of factual knowledge stored in LLMs. Instead of relying on an off-the-shelf benchmark, we propose to generate a diverse test set containing different types of questions at varying difficulty levels to reduce the impact of benchmark contamination on the evaluation results.\n\n> **failed to address another crucial concern: the model's sensitivity to prompts/templates**\n\nWe would like to point out that our proposed framework is orthogonal to the model prompting techniques. Users have the flexibility to design the prompts to interact with the LLMs. \n\nHowever, we agree that LLMs tend to be sensitive to the prompts and templates. Therefore, for each LLM, we use the instruction template, which has a similar structure to its instruction finetuning template, to avoid the discrepancy between instruction finetuning and testing. We use the same prompts for all LLMs to ensure the results' consistency. It is indeed an interesting analysis on prompt sensitivity. As it is not our main objective, we leave the exploration of the evaluation prompts as future works.\n\n> **Influence of context information on model predictions. The author should incorporate a discussion of these works**\n\nWe thank the reviewer for pointing out the missing references. We will incorporate them in the camera ready (if accepted).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* Due to its relation to the issue of model hallucination, the factual knowledge in large-scale models is a matter of great concern. The accurate and unbiased evaluation of factual knowledge within language models is an important research direction.\n* This work provides relevant questions and metrics for evaluating the knowledge of language models. It also compares different models, which contributes to a better understanding of Large Language Models and will be beneficial for future evaluations of other models.", "reasons_to_reject": "* My major concern is whether the benchmark proposed in the paper can genuinely assess factual knowledge in language models. In my opinion, Alpaca and Vicuna are just fine-tuned versions of llama on instruction datasets, which do not involve a significant amount of factual knowledge. Therefore, the factual knowledge in Alpaca and Vicuna should not differ significantly from llama. However, in the paper's experiments, there is a substantial performance gap between these models (34 V.S. 2.1). In other words, the benchmark might be evaluating the models' ability to follow instructions rather than accurately assessing their factual knowledge. Does this also imply that the benchmark is not suitable for evaluating pre-trained language models without instruction tuning?\n* About the motivation of this paper:: The author believes that other benchmarks may be affected by the overlap between the model's pre-training data and testing data. However, I don't understand why the evaluation method proposed in this paper would not be affected by such overlap [1].\n* About the diversity of questions：The author modified the format of the questions but failed to address another crucial concern, which is the model's sensitivity to prompts/templates [1][2][3]...\n* Regarding the novelty of the conclusions, lots of previous works have already discussed the influence of context information on model predictions. The author should incorporate a discussion of these works[4][5][6]....\n\n[1] Can Prompt Probe Pre-trained Language Models? Understanding the Invisible Risks from a Causal View\n\n[2] Measuring and Improving Consistency in Pretrained Language Models \n\n[3] Calibrate Before Use: Improving Few-shot Performance of Language Models\n\n[4] How Context Affects Language Models' Factual Predictions\n\n[5] Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?\n\n[6] A Drop of Ink Makes a Million Think: The Spread of False Information in Large Language Models", "questions_to_authors": "Please refer to reason to reject.", "paper_topic_and_main_contributions": "This paper proposes a framework to evaluate the factual knowledge in LLMs. They first generate three kinds questions from tuples in knowledge graph, and evaluate the accuracy of different LLMs in answering these questions. By comparing the performance of several LLMs, and they find that ChatGPT outperforms other LLMs, and the performance depends on instruction fine-tuning, domain, context, etc.", "excitement": 2, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Please refer to reason to reject."}, "extra_text": {}}, {"review_id": "Szc74rpAr3", "reviewer": "Reviewer_ezC3", "summary": "", "questions": "A: line 119, how do you assure the entity is wrong? do you use entities of the same type or different type? It is important to give more details about these wrong entities.\nB: line 127, \"incorrect entities\": the same questions given above", "limitations": "", "rating": 3, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for reviewing our paper and providing insightful feedback. We have carefully considered your points and prepared responses to address your concerns.\n\n> **why it is important to assess factual knowledge in LLMs besides, or beyond, other types of knowledge?**\n\nAssessing factual knowledge in Large Language Models (LLMs) such as ChatGPT is important for several reasons.\n- Factual knowledge refers to information that is objectively true and can be verified against empirical evidence, which is crucial in various downstream tasks, e.g., question answering. \n- Evaluating the factual knowledge of LLMs can ensure that the model is providing reliable and trustworthy information to users. This is crucial, especially in contexts where misinformation or false information can have real-world consequences.\n- Assessing the factual knowledge can provide us with a general understanding of the ability of LLMs before we put them into to actual applications. For example, an LLM without knowledge of the medical domain cannot be applied to the medical diagnosis task.\n- Factual knowledge is a general and fundamental type of knowledge. Other types of knowledge, e.g., commonsense knowledge and reasoning knowledge, can be grouped under factual knowledge or relied on. Thus, assessing factual knowledge is a fundamental task in evaluating LLMs. In the future, we will also explore evaluating other knowledge in LLMs.\n\n> **There is no related work.**\n\nDue to the page constraint, we briefly mentioned the related works on evaluating LLMs in the introduction. We will add a related work section in camera ready (if accepted) when we have an additional page.\n\n> **Authors should analyze, and comments, this and include also recall scores in the paper**\n\nThe abstention rate is highlighted in color cell of Table 1. We will improve our presentation and add recall scores in the camera ready (if accepted). \n\n>  **True-False Question, how do you assure the entity is wrong? do you use entities of the same type or different type?**\n\nThe KGs studied in our paper do not have type attributes associated with the entities, i.e. there is only a single entity type for each relation.  We first gather all the entities from triples that fall under a specific relation to form a candidate set. This helps us group entities with similar characteristics. To generate false questions, we randomly choose entities from the candidate set that are not linked to the subject under this relation as the incorrect object.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The paper is clear and contains lot of experiments and information for evaluating LLMs. \n- The authors evaluate several LLMs\n- The authors use several datasets\n", "reasons_to_reject": "- I miss a deeper motivation for assessing factual knowledge instead other types of knowledge. After referring in the introduction to other approaches focused on assessing language and knowledge capabilities, you should explain why you proposed to assess factual knowledge instead. I mean, why it is important to assess factual knowledge in LLMs besides, or beyond, other types of knowledge?\n- There is no related work.\n- Line 224, \"Overall, ChatGPT achieves the best average precision. However, it also has a high percentage of abstained answers across all question types.\"-> For other systems, besides ChatGPT, the more abstained answers, the better precision. Authors should analyze, and comments, this and include also recall scores in the paper (I only see precision and F1)\n", "questions_to_authors": "A: line 119, how do you assure the entity is wrong? do you use entities of the same type or different type? It is important to give more details about these wrong entities.\nB: line 127, \"incorrect entities\": the same questions given above", "paper_topic_and_main_contributions": "The paper proposes a method for evaluating the factual knowledge in LLMs. The authors describe a method for creating a collections of questions, in different formats,  from a KB and apply it to several KBs to evaluate several LLMs. \n\nThe main contributions of this paper are:\n\n- the method for creating questions from KBs\n- the use of different format of questions for the same facts\n- evaluation of different families of LLMs\n- evaluation of different inputs to the LLMs\n- measure abstention", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "- line 23, \"large language models (LLMs)\" -> \"Large Language Models (LLMs)\". There are several examples across the paper  (NLP, KGs) ... You must capitalized the first letter of each word.\n\n- Line 70, \"LLMs performance depends on several factors such as instruction finetuning, domains, and question complexity. Despite sharing the same parametric knowledge base, models finetuned with different instruction datasets show varying performance levels. In general076\ndomain KGs, LLMs achieve the highest score, but their performance declines in specialized domains and is worse on questions having a wide range of potential answers.\" -> this is an observation or conclusion from your work, not a contribution. The contribution might be to obtain such conclusion. Something like \"We show how LLMs performance ...\"\n"}}]

Author rebuttal:

Meta-review: The paper proposes a framework to systematically evaluate factual knowledge of LLMs with KGs in generic and specific domains. All reviewers appreciated that the analysis in the paper is comprehensive, covering various LLMs, question types, and domains. Reviewers highlighted how the approach takes into account abstentions by the LLMs.

Reviewers also expressed some concerns about the experiment setup. For instance, it's not clear if the evaluation might be conflating models' ability to follow instructions with factual knowledge. Another issue that reviewers raised is whether the proposed benchmark also has ‘leakage problems’ similar to past benchmarks. One reviewer pointed out that automatically generating questions from knowledge graphs has been explored in past work (see reviews for references) and shouldn’t be considered a major contribution of this paper.
Decision: Accept-Findings