SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Authors: Potsawee Manakul
Adian Liusie
Mark Gales
Venue: EMNLP 2023 Main (2023)
Keywords: generative-AI hallucination
fact-checking
trustworthy artificial intelligence
TL;DR: This work studies LLM hallucinations. We propose SelfCheckGPT, a sampling-based approach to fact-check the responses of black-box models in a zero-resource fashion, i.e. without an external database.

Abstract: Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate facts and make non-factual statements which can undermine trust in their output. Existing fact-checking approaches either require access to the output probability distribution (which may not be available for systems such as ChatGPT) or external databases that are interfaced via separate, often complex, modules. In this work, we propose "SelfCheckGPT", a simple sampling-based approach that can be used to fact-check the responses of black-box models in a zero-resource fashion, i.e. without an external database. SelfCheckGPT leverages the simple idea that if an LLM has knowledge of a given concept, sampled responses are likely to be similar and contain consistent facts. However, for hallucinated facts, stochastically sampled responses are likely to diverge and contradict one another. We investigate this approach by using GPT-3 to generate passages about individuals from the WikiBio dataset, and manually annotate the factuality of the generated passages. We demonstrate that SelfCheckGPT can: i) detect non-factual and factual sentences; and ii) rank passages in terms of factuality. We compare our approach to several baselines and show that our approach has considerably higher AUC-PR scores in sentence-level hallucination detection and higher correlation scores in passage-level factuality assessment compared to grey-box methods.

SELFCHECKGPT:
Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Potsawee Manakul, Adian Liusie, Mark J. F. Gales ALTA Institute, Department of Engineering, University of Cambridge pm574@cam. ac. uk, al8260cam.c ac. uk, mjfg@eng. cam ac. uk
Abstract
Stochattiallly-geerrate responses
Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly a wide variety of user prompts. However, LLMs are known to hal- lucinate facts and make non-factual statements which undermine trust in their output. Ex- isting fact-checking approaches either require access to the output probability distribution (which may not be available for systems such as CharGPT) or external databases that are in- terfaced via separate, often complex, modules. In this work, propose "SPI simple sampling- basee approach that can be used to fact-check the responses of black-box models in zero-resource fashion, i.e. with- out an external database. SelfCheckGPT lever- ages the simple idea that if LLM has knowl- edge of given concept, sampled responses are likely to be similar and contain consistent facts. However, for hallucinated facts, stochas- tically sampled responses are likely to diverge and contradict one another. We investigate this approach by using GPT-3 to generate passages about individuals from the WikiBio dataset, and manually annotate the factuality of the gener- ated passages. We demonstrate that SelfCheck- GPT can: i) detect non- non-factual and factual sen- tences; and ii) rank passages in terms of factu- ality. We compare our approach to several hase- lines and show that our approach has consider- ably higher AUC-PR scores in sentence-lever hallucination detection and higher correlation scores in passage-level factuality assessment compared to grey-box methods.
LLM e.g. GPT-3
sample1
sampleN
Glusepon Marariwass
Capsespo Morani vidinat
tnikn paimee souptee
eggaes He
pediggngue and
fluent responses
1662, ded Paris.
Paraa RUls Junt [ponssee)]
Nample
Tranea, in 1964. Jourcated]
Marian Italian professional footba for who played an Inrwnd.. Hin was born in Mian Itlly He dee Rome, tay. [Huncated]
LLM
(Smplc))) suppoit pantsnoc??
Sempich) susport paononoee [Meuttcc
Arowor (Yaahho
Min passage evolustedd of pertovice-oval
No
Yee
No
SellCheckGPT Score (e how often the sontence supported by the camples)
Figure I: SelfCheckGPT with Prompt. Fach LLM-genenann sentence is compared against stochastically generated re- sponses with no extermal database. A comparison method can be, for example, through LLM prompting as shown above.
tools to draft reports, virtual assistants and sum- marization systems. Despite the convincing and realistic nature of LLM-generated texts, growing concern with LLMs is their tendency to halluci- nate facts. has been widely observed that mod- els can confidently generate fictitious information, and worryingly there are few, if any, existing ap- proaches to suitably identify LLM hallucinations.
A possible approach of hallucination detection is to leverage existing intrinsic uncertainty metrics to determine the parts of the output sequence that the system least certain of (Yuan al., 2021; Fu et al., 2023). However, uncertainty metrics such as token probability or entropy require access to teken-level probability distributions, information which may not be available to users for example when systems are accessed through limited exter- nal APIs. An alternate approach is to leverage fact-verification approaches, where evidence is trieved from an external database to assess the ve- racity of a claim (Thorne et al., 2018: Guo et al., 2022). However, facts can only be assessed relative to the knowledge present in the database. Addition-
1 Introduction
Large Language Models (LLMs) such as GPT-3 (Brown et al., 2020) and PaLM (Chowdhery et al., 2022) are capable of generating fluent and realistic responses to a variety of user prompts. They have been used in many applications such as automatic
'Code and datasct can he found on the project page at https://gitthu com/ wwtpppwwtswweeeeppoppp sel fcheckgpt.

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ally, hallucinations are observed over wide range of tasks beyond pure fact verification (Kryscinski et al., 2020; Maynez et al., 2020).
internal states of the LLM, which may not be avail- able through API calls, and requires labelled data for supervised training. Another recent approach is self-evaluation (Kadavath et al., 2022), where an LLM is prompted to evaluate its previous predic- tion, e.g., to predict the probability that its gener- ated response/answer is true.
In this paper, we propose SelfCheckGPT, a sampling-based approach that can detect whether responses generated by LLMs are hallucinated or factual. To the best of our knowledge, SelfCheck- GPT is the first work to analyze model halluci- nation of general LLM responses, and is the first zero-resource hallucination detection solution that can be applied to black-box systems. The motivat- ing idea of SelfCheckGPT that when an LLM has been trained on a given concept, the sampled re- sponses are likely to be similar and contain consis- tent facts. However, for hallucinated facts, stochas- tically sampled responses are likely to diverge and may contradict one another. By sampling multiple responses from an LLM, one can measure informa- tion consistency between the different responses and determine if statements are factual or halluci- nated. Since SelfCheckGPT only leverages sam- pled responses, it has the added benefit that it can be used for black-box models, and it requires no external database. Five variants of SelfCheckGPT for measuring informational consistency are con- sidered: BERTScore, question-answering, n-gram, NLI, and LLM prompting. Through analysis of an- notated articles generated by GPT-3, we show that SelfCheckGPT a highly effective hallucination detection method that can even outperform grey- box methods, and serves strong first baseline for an increasingly important problem of LLMs.
2.2 Sequence Level Uncertainty Estimation
Token probabilities have been used as an indica- tion of model certainty. For example, OpenAT's GPT-3 web interface allows users to display token probabilities (as shown in Figure 2), and further un- certainty estimation approaches based on aleatoric and epistemic uncertainty have been studied for autoregressive generation (Xiao and Wang, 2021; Malinin and Gales, 2021). Additionally, condi- tional language model scores have been used evaluate properties of texts (Yuan et al., 2021; Fu et al., 2023). Recently, semantic uncertainty has been proposed to address uncertainty in free-form generation tasks where probabilities are attached to concepts instead of tokens (Kuhn et al., 2023).
Lemnu Fande Benttan thittt Ameitcan homei peresslent Deseba
oagin meeaa MMue lgai duee
an sutaa Nithiaal vanntae tesss singen
Deatle 44%
Einer as in wekir me tene nereec wit
3.47 oo
2 Background and Related Work
Look to Chatd17
Thytnnew
2.1 Hallucination of Large Language Models
Figure 2: Example OpenAl's GPT-3 weh interface with output token-leve probabilities displayed.
Hallucination has been studied in text generation tasks, including summarization (Huang et al., 2021) and dialogue generation (Shuster et al., 2021), as well as in variety of other natural language gen- eration tasks (Ji et al., 2023). Self-consistency decoding has shown improve chfin-of-thouuht prompting performance on complex reasoning tasks (Wang et al., 2023). Further, Liu et al. (2022) introduce a hallucination detection dataset, how- ever, texts are obtained by perturbing factual texts and thus may not reflect true LLM hallucination. Recently, Azaria and Mitchell (2023) trained multi-layer perception classifier where LLM's hidden representations are used as inputs to pre- dict the truthfulness of sentence. However, this approach is a white-box approach that uses the
2.3 Fact Verification
Existing fact-verification approaches follow a multi-stage pipeline of claim detection, evidence retrieval and verdict prediction (Guo al., 2022; Zhong al., 2020). Such methods, however, re- quire access to external databases and can have considerable inference costs.
Grey-Box Factuality Assessment
This section will introduce methods that can be used to determine the factuality of LLM responses in a resouresource setting when one has full access

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output distributions. We will use 'factual' to define when statements are grounded in valid infor- mation, i.e. when hallucinations are avoided, and "zero-resource" when no external database used.
Entropy
The entropy of the output distribution is:
Hij
Σ Piy(ū) log Piy( NEW
3.1 Uncertainty-bazt Assessment
To understand how the factuality of generated response can be determined in a zero-resource set- ting, we consider LLM pre-training. During pre- training, the model is trained with next-word pre- diction over massive corpora of textual data. This gives the model a strong understanding of language (Jawahar et al., 2019; Raffel al., 2020), power- ful contextual reasoning (Zhang et al., 2020), as well as world knowledge (Liusie et al., 2023). Con- sider the input "Lionel Messi is _". Since Messi is world-famous athlete who may have appeared multiple times in pre-training, the LLM is likely to know who Messi is. Therefore given the context, the token "footballer" may be as- signed high probability while other professions such as "carpenter" may be considered improba- ble. However, for a different input such as "John Smith is a _", the system will be unsure of the continuation which may result in a flat probability distribution. During inference, this is likely to lead to non-factual word being generated.
where Piy Piy(w) ) is the probability of the word i being generated at the j-th token of the i-th sentence, and W is the set of all possible words in the vocabu- lary. Similar to the probability- metrics, two entropy- entropy-based metrics are used:
Avg(H) H
Hej Max(H)- max (Hzz) J
4 Black-Box Factuality Assessment
A drawback of grey-box methods is that they re- quire output token-level probabilities. Though this may seem a reasonable requirement, for massive LLMs only available through limited API calls, such token-level information may not be available (such as with ChatGPT). Therefore, we consider black-box approaches which remain applicable even when only text-based responses are available.
Proxy LLMs
This insight allows to understand the con- nection between uncertainty metrics and factuality. Factual sentences are likely contain tokens with higher likelihood and lower entropy, while halluci- nations are likely to come from positions with flat probability distributions with high uncertainty.
A simple approach to approximate the grey-box approaches is by using a proxy LLM, i.c. another LLM that we have full access to, such as LLaMA (Touvron et al., 2023). A proxy LLM can be used to approximate the output token-level probabilities of the black-box LLM generating the text. In the next section, we propose SelfCheckGPT, which is also black-box approach.
Token-level Probability
Given the LLM's response Rt, let denote the i-th sentence in R, j denote the j-th token in the i-th sentence, J is the number of tokens in the sentence, and Pij be the probability of the word generated by the L.M the j-th token the i-th sentence. Two probability metrics are used:
5 SelfCheckGPT
SelfCheckGPT is our proposed black-box zero- resource hallucination detection scheme, which op- erates by comparing multiple sampled responses and measuring consistency.
1 Σ log Pij
log -
Notation: Let R refer to an LLM response drawn from a given user query. SelfCheckGPT draws further N stochastic LLM response sam- ples { S2,,.., S"....,....... S using the same query, and then measures the consistency between the response and the stochastic samples. We design SelfCheckGPT to predict the hallucination score of the i-th sentence, S(i), such that S(i) E [0.0, 1.0), where S(2) - 0.0 if the i-th sentence is grounded in valid information and S(i) - 1.0 if the i-th sen-
Max( log max (- log Pi))
3
Mox(-logp) measures the sentence's likelihood by assessing the least likely token in the sentence.
AAltemaae white-box approaches such as that of Azaria and Mitchell (2023) require access to full internal states, and is less practical and so not considered in this work.

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lence is hallucinated.? 3 The following subsections will describe each of the SeliCheckGPT variants.
N: No.. To take into account the answerability of N,+N," generated questions, we show in Appendix B that we can modify the inconsistency score by applying soft-counting, resulting in:
5.1 SelfCheckGPT with BERTScore
Let ((...) denote the BERTScore between two sen- tences. SelfCheckGPT with BERTScore finds the average BERT'Score of the i-th sentence with the most similar sentence from each drawn sample:
N
SQA
(5)
NZ
11 + 72
where N2 = the effective match count, N the effective mismatch count, with "n and "2 defined in Appendix B.1. Ultimately, SelfCheckGPT with QA is the average of inconsistency scores across d
max (B(ri, s()) 1 N
SBERT (i)
&
where r; represents the i-th sentence in and sh represents the k-th sentence in the n-th sample S This way if the information in a sentence appears in many drawn samples, one may assume that the information is factual, whereas if the statement ap- pears in other sample, it is likely hallucination. In this work, RoBERTa-Large (Liu al., 2019) is used the backbone of BERTScore.
SQA (i) = B.g [SQa 2)
(6)
5.3 SellCheckGPT with n-gram
Given samples ['' generated by an LLM, one can use the samples to create a new language model that approximates the LLM. In the limit as N gets sufficiently large, the new language model will converge the LLM that generated the sponses. We can therefore approximate the LLM's token probabilities using the new language model.
5.2 SelfCheckGPT with Question Answering We also consider using the automatic multiple- choice question answering gencration (MQAG) framework (Manakul et al., 2023) to measure con- sistency for SelfCheckGPT. MQAG assesses con- sistency by generating multiple-choice questions over the main generated response, which an inde- pendent answering system can attempt to answer while conditioned the other sampled responses. If questions on consistent information are queried, the answering system is expected predict similar answers. MQAG consists of two stages: question generation and question answering A. For the sen- tence in the response R, we draw questions and options
In practice, due to time and/or cost constraints, there can only be a limited number of samples N. Consequently, we train simple n-gram model us- ing the samples { S as well as the main response R (which is assessed), where we note that including R. can be considered method where the count of each token in R is in- creased by 1. We then compute the average of the pog-pobbaillitis of the sentence in response R.
smoothing
Sy-gram (i) - Σ J
log P.j
(7)
where Pij is the probability (of the j-th token of the (2) i-th sentence) computed using the n-gram model. Similar to the grey-box approach, we can also use the maximum of the negative log probabilities,
q. o Pa(g.o/ri,,
The answering stage A selects the answers:
0g argmax [Pa o))
Max n grggram
(i) = max (-log
(8)
ag argmax (Palo S".)))
(4)
5.4 SelfCheckGPT with NLI
Natural Language Inference (NLI) determines whether a hypothesis follows premise, classified into either enlaimennd/reuttlloontttiiiiiiii measures have been used to measure faithfulness. in summarization, where Maynez al. (2020) use textual entailment classifier trained on MNLI (Williams et al., 2018) to determine a summary contradicts a context or not. Inspired by NLI-based
We compare whether is equal to age for each sample in {S) .... S. yielding #mmttchs N₂, and #not-matches N.. A simple inconsistency score for the i-th sentence and question based the match/not-mateh counts is defined: SQA (i)
NLI
'With the exception of SelfCheckGPT with n-gram as the score of the n-gram language model is not bounded.

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summary assessment, we consider using the NLI contradiction score SelfCheckGPT score. For SelfCheck-NLI, we use DeBeRTa-v3-large (He et al., 2023) fine-tuned to MNLI as the NLI model. The input for NLI classifiers typically the premise concatenated to the hypothesis, which for our methodology is the sampled passage S" concatenated to the sentence to be assessed ri. Only the logits associated with the 'entailment' and 'contradiction' classes are considered,
Data and Annotation
As, currently, there are no standard hallucination detection datasets available, we evaluate our hallu- cination detection approaches by 1) generating syn- thetic Wikipedia articles using GPT-3 on the indi- viduals/concepts from the WikiBio dataset (Lebret et al., 2016); manually annotating the factuality of the passage at sentence level; 3) evaluating the system's 's ability detect hallucinations.
WikiBio is a dataset where each input contains the first paragraph (along with tabular information) of Wikipedia articles of a specific concept. We rank the WikiBio test set in terms of paragraph length and randomly sample 238 articles from the top 20% of longest articles (to ensure very obscure concept is selected). GPT-3 (text-davinci-003) is then used generate Wikipedia articles on a con- cept, using the prompt "This a Wikipedia passage about {concept]:". Table provides the statistics of GPT-3 generated passages.
exp(ze)
(. exp(ze) + exp(ze)
where 2e and 2e are the logits of the 'entailment' and 'contradiction' classes, respectively. This nor- malization ignores the neutral class and ensures that the probability is bounded between 0.0 and 1.0. The SelfCheckGPT with NLI score for each sample 5th is then defined as.
N
(NL( =
Σ
P(contradict|rg.5) S*) (10)
N
#Passages
#Sentences # #Tokens/passage
5.5 GeI with Prompt
238
1908
184.7+36.9
LLMs have recently been shown to be effective in assessing information consistency between doc- ument and its summary in zero-shot settings (Luo al., 2023). Thus, we query LLM to assess whether the i-th sentence is supported by sample S" (as the context) using the following prompt.
Table 1: The statistics of WikiBio GPT-3 dataset where the number of tokens hased on the OpenAl GPT-2 tokenizer.
We then annotate the sentences of the generated passages using the guidelines shown in Figure 3 such that each sentence classified as either:
Context: ()
Major Inaccurate (Non-Factual, 1): The sen- tence is entirely hallucinated, i.e. the sentence is unrelated to the topic.
Sentence: ()
Is the sentence supported by the context above? Answer Yes or No:
Minor Inaccurate (Non-Factual, 0.5): The sentence consists of some non-factual infor- mation, but the sentence is related to the topic.
Initial investigation showed that GPT-3 (text- davinci-003) will output either Yes or No 98% of the time, while any remaining outputs can be set to N/A. The output from prompting when comparing the i-th sentence against sample S is converted to score zel through the mapping (Yes: 0.0, No: 1.0, N/A: 0.5). The final inconsistency score is then calculated as:
Accurate (Factual, 0): The information pre- sented in the sentence is accurate.
Of the 1908 annotated sentences, 761 (39.9%) of the sentences were labelled major-inaccurate, 631 (33.1% minor-inaccurate, and 516 .0.%) accu- rate. 201 sentences in the dataset had annotations from two different annotators. To obtain a single la- bel for this subset, if both annotators agree, then the agreed label is used. However, if there is disagree- ment, then the worse-case label is selected (e.g.. (minor inaccurate, major inaccurate) is mapped major inaccurate). The inter-annotator agreement, as measured by Cohen's x (Cohen, 1960), has к
N
S Sprompt (1)
x
(11)
N
m=1
SelfCheckGPTT-romee is illustrated in Figure 1. Note that our initial investigations found that less capable models such as GPT-3 (text-curie-001) or LLaMA failed to effectively perform consistency assessment via such prompting.

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N=20 samples. For the proxy LLM approach, we use LLaMA (Touvron et al., 2023), one of the best- performing open-source LLMs currently available. For SelfChekGGPT-Prompt we consider both GPT- (which is the same LLM that is used to generate passages) as well as the newly released ChatGPT More details about the systems in SelfCheckGPT and results using other proxy LLMs can be found in the appendix.
Is It related to
No
Major Inaccurate (Non-factual 1)
Yes
Is Faclual?
No
Minor Inaccurate (Non-factual 0.5)
g. using Wikipedia/
Google Search
Yes
7.1 Sentence-level Hallucination Detection
Accurate (Factual
First, we investigate whether our hallucination de- tection methods can identify the factuality of sen- tences. In detecting non-factual sentences, both major-inaccurate labels and minor-inaccurate la- bels are grouped together into the non-factual class, while the factual class refers to accurate sentences. In addition, we consider more challenging task of detecting major- mujjr--naccurate sentences in passages that not total hallucination passages, which we refer as non-factual" Figure and Table 2 show the performance of our approaches, where the following observations can made:
Figure 3: Flowchart of our annotation process
values of 0.595 and 0.748, indicating moderate and substantial agreement (Viera et al., 2005) for the 3-class and 2-class scenarios, respectively.' Furthermore, passage-level scores are obtained by averaging the sentence-level labels in each pas- sage. The distribution of passage-level scores is shown in Figure 4, where we observe a large peak at +1.0. We refer to the points at this peak as total hallucination, which occurs when the information of the response is unrelated to the real concept and is entirely fabricated by the LLM.
1) LLM's probabilities correlate well with factuality. Our results show that probability mea- sures (from the LLM generating the texts) are strong baselines for assessing factuality. Factual sentences can be identified with AUC-PR of 53.97, significantly better than the random baseline of 2f.04. 04, with the AUC-PR for hallucination detec- tion also increasing from 72.96 to 83.21. This sup- ports the hypothesis that when the LLMs are uncer- tain about generated information, generated tokens often have higher uncertainty, paving a promising direction for hallucination detection approaches. Also, the probability p measure performs better than the entropy H. measure of top-5 tokens.
30
Count
0.0
0.2
0.4
0.6
0.8
1.0
Ractuaiity
F0-Factull, +1-Non
2) Proxy LLM perform noticeably worse than LLM (GPT-3). The results proxy LLM (based on LLaMA) show that the entropy H. measures outperform the probability measures. This sug- gests that using richer uncertainty information can improve factualityhhallucination detection perfor- mance, and that previously the entropy of top-5 tokens is likely to be insufficient. In addition, when using other proxy LLMs such as GPT-NeoX ОPT-30B, the performance is near that of the ran- dom baseline. We believe this poor performance occurs as different LLMs have different generating patterns, and so even common tokens may have a
Figure 4: Document factuality scores histogram plot
7 Experiments
The generative LLM used to generate passages for our dataset is GPT-3 (text-davinci-003. the state- of-the-art system at the time of creating and anno- tating the dataset. To obtain the main response, set the temperature to 0.0 and use standard beam search decoding. For the stochastically generated samples, we set the temperature to 1.0 and generate
33-class refers to when selecting between accurate, mi- nor inaccurate, major inaccurate. 2-class refers to when mi- nor/major inaccuracies are combined into one lahel.
"There are 206 non-factual* passages (1632 sentences).

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1.0
Barese
Setck sont
GPT-3 Hegg?
0.9
3.9
6.45
Saltik De
ScrCck QA
0.5
38
SerCe-un grain
5.02
Setck
Seftck muintt
SclCck horp!
SatCk-Nu
677
0.45
6.6
15
6.5
6.75
0.75
12-
Foccal
Rocal
(a) Non-Factual Sentences
(b) Non-Factual Sentences
(c) Factual Sentences
Figure 5: PR-Curve of detecting non-factual and factual sentences in the GPT-3 generated WikiBio passages.
Sentence-level (AUC-PR)
Passage-level (Corr.)
Method
NonFact N
Factual
Pearson
Spearman
Random
72.96
29.72
27.04
GPT-3 (text-davinci-003) probabilities (LLM,, grey-box)
Avg(-logy)
83.21
38.89
53.97
57.04
53.93
Avg(?()
80.73
37.09
52.07
55.52
50.87
Max(-logp) 87.51
35,88
50.46
57.83
55.69
Max(H)
85.75
32.43
50.27
52.48
49.55
LLaMA-30B's probabilities (Proxy LLM, black-box)
Avg(-logg)
75.43
30.32
41.29
21.72
20.20
Avg(H))
80.80
39.01
42.97
33.80
39.49
Max(-log)) 74.01
27.14
31.08
-22.83
-22.71
Max(HL)
80.92
37.32
37.90
35.57
38.94
Sel[CheckGPT (black-box)
w/ BERTScore
81.96
45.96
44.23
58.18
55.90
w/ QA
84.26
40.06
48.14
61.07
59.29
w/ Unigram (max)
85.63
41.04
58.47
64.71
64.91
w/ NLI
92.50
45.17
66.08
74.14
73.78
w/ Prompt
93.42
53.19
67.09
78.32
78.30
Table 2: AUC-PR for sentence-leve detection tasks. Passage- ranking performances are measured by Pearson correlation coefficient and Spearman's rank correlation coefficient w.r.. human judgements. The results of other proxy L.MMs, addition to LLaMA, can be found in the appendix. 'GPT-3 API returns the top-5 tokens' probabilities, which are used to compute entropy.
low probability in situations where the response is dissimilar to the generation style of the proxy LLM. We note that weighted conditional LM such as BARTScore (Yuan et al., 2021) could be incorporated in future investigations.
across different setups. Essentially, when assessing sentence, this method picks up the token with the lowest occurrence given all the samples. This suggests that if token only appears few times (or once) within the generated samples (N=20), it likely non-factual.
3) SelFCheckGPT outperforms grey-box ap- proaches. It can be seen that SelfCheckGPT- Prompt considerably outperforms the grey-box ap- proaches (including GPT-3's output probabilities) well other black-box approaches. Even other variants of SeIPCheckGPT, including BERTScore, QA, and n-gram, outperform the grey-box ap- proaches in most setups. Interestingly, despite be- ing the least computationally expensive method, SelfCheckGPT with unigram (max) works well
4) SelICheckGPT with n-gram. When inves- tigating the n-gram performance from 1-gram to 5-gram, the results show that simply finding the least likely token/n-gram is more effective than computing the average n-gram score of the tence, details in appendix Table 7. Additionally, as n increases, the performance of SelICheckGPT with n-gram (max) drops.
5) SelFCheckGPT with NLI. The NLI-based

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0.7
10.
0.4
20
Mobhod Score
0.3
6.4
0.2
0.1
0.0
0.-
0.2
0.6
1.0
0.2
0.4
0.5
1.0
0.0
0.2
0.6
0.8
Human Score (Cmiaclaa) fon-faccaaa)
Scare (C-faciua) FaMMurFacllall
Human Scare (D-Factual, #1-Rur-factual
(a) GPT-3 Avg( log p)
(b) L08 Avg(H)
(c) SelfCheckGPT-Prompll
Figure Scatter plot passage-level scores where Y-axis Method scores, X-axis Human scores. Correlations are reported in Table The seatter plats other SelfCheckGiPT variants are provided in Figure 10 the appendix.
method outperforms all black-box and grey-box baselines, and its performance is close to the per- formance of the Prompt method. As SelfCheck- GPT with Prompt can be computationally heavy, SelfCheckGPT with NLI could be the most practi- cal method it provides good trade-off between performance and computation.
7.3 Ablation Studies
External Knowledge (instead of SelfCheck)
If external knowledge is available, one can measure the informational consistency between the LLM response and the information source. In this exper- iment, we use the first paragraph of each concept that available in WikiBio."
7.2 Passage-level Factuality Ranking
Sent-Ivl AUC-PR
Passage-Ivl
Method
Previous results demonstrate that SelfCheckGPT is an effective approach for predicting sentence- level factuality. An additional consideration is whether SelfCheckGPT
NoFac NoFac*
Fact
Pear.
Spear.
ScIECk-BERT WikiBio+BERT
81.96
45.96
44.23
58.18
55.90
81.32
40.62
49.15
58.71
55.80
also be used to de- termine the overall factuality of passages. Passage- level factuality scores are calculated by averaging the sentence-level scores over all sentences.
SelfCk-QA WikiBio+QA
84.26
40.06
48.14
61.07
59.29
84.18
45.40
52.03
57.26
53.62
SelfCk-Igm WikiBio+ Igm
85.63
41.04
58.47
64.71
64.91
80.43
31.47
40.53
28.67
26.70
SelfCk-NLI
92.50
45.17 17
66.08
74. 14
73.78
WikiBio+NLI
91.18
48.14
71.61
78.84
80.00
1 R
Sp Spassage
S(())
S(i
(12)
SeliCk-Prompt
93.42
53.19
67.09
78.32
78.30
WikiBiotPrompt
93.59
65.26
73.11
85.90
86.11
where S(i) is the sentence-level score, and R is the number of sentences in the passage. Since
Table The performance when using SelfCheckGPT samples versus external stored knowledge.
human judgement is somewhat subjective, averag- ing the sentence-level labels would lead to ground truths with less noise. Note that for Avg(- log p) and Avg(H), we compute the average over all to- kens in passage. Whereas for Max(- p) and Max(H), first take the maximum operation over tokens at the sentence level, and we then average over all sentences following Equation 12.
Our findings in Table 3 show the following. First, SelfCheckGPT with BERTSCorr/QA, using self- samples, yield comparable even better per- formance than when using the reference passage. Second, SelfCheckGPT with n-gram shows a large performance drop when using the WikiBio pas- sages instead of samples. This failure at- tributed to the fact that the WikiBio reference text alone is not sufficient to train an n-gram model. Third, in contrast, SelfCheckGPT with NLI/Prompt can benefit considerably when access to retrieved information is available. Nevertheless, in practice,
Our results in Table 2 and Figure 6 show that all SelfCheckGPT methods correlate far better with human judgements than the other baselines, in- cluding the grey-box probability and entropy meth- the best-performing method, achieving the highest Pearson correlation 78.32. Unsurprisingly, the proxy LLM approach again achieves considerably lower correlations.
ods. SelfCheckGPT-Prommee
"This method no longer zero-resouree as it requires retrieving relevant knowledge from external data.

---

is infeasible to have an external database for ery possible use case of LLM generation.
Conclusions
This paper the first work to consider the task of hallucination detection for general large lan- guage model responses. We propose SelfCheck- GPT, a zero-resource approach that is applicable to any black-box LLM without the need for ex- ternal resources, and demonstrate the efficacy of our method. SelfCheckGPT outperforms range of considered grey-box and black-box baseline de- tection methods at both the sentence and passage levels, and further release an annotated dataset for GPT-3 hallucination detection with sentence- level factuality labels.
The Impact of the Number of Samples
Although sample-bases methods are expected to perform better when more samples are drawn, this has higher computational costs. Thus, we inves- tigate performance as the number of samples is varied. Our results in Figure 7 show that the per- formance of SeIICheckGPT increases smoothly as more samples used, with diminishing gains as more samples are generated. Sel/CheckGPT with n-gram requires the highest number of samples before its performance reaches plateau.
Limitations
80'
10
In this study, the 238 GPT-3 generated texts were predominantly passages about individuals in the WikiBio dataset. To further investigate the nature of LLM's hallucination, this study could be ex- tended to a wider range of concepts, e.g., to also consider generated texts about locations and ob- jects. Further, this work considers factuality at the sentence level, but note that single sentence may consist of both factual and non-factual infor- mation. For example, the following work by Min et al. (2023) considers fine nee-grained factuality eval- uation by decomposing sentences into atomic facts. Finally, SelfCheckGPT with Prompt, which was convincingly the best selfcheck method, is quite computationally heavy. This might lead to impracti- cal computational costs, which could be addressed in future work be made more efficient.
Ramaic
00
SelfCk- Scor
SeliCk-GA
SelfCk-uii
Prompt
12 samples
16
Figure 7: The performance Scl[CheckGPT methods on ranking passages (Spearman 's) versus the numher samples.
The Choice of LLM for SefCChecGGPT-Prompp
We investigate whether the LLM generating the text can self-check its own text. We conduct this ablation using reduced set of the samples (N=4).
Ethics Statement
Text-Gen
SelfCk- SelfCk-Prompt
N
Pear.
Spear.
GPT-3
ChatGPT
20
78.32
78.30
As this work addresses the issue of LLM's halluci- nation, we note that if hallucinated contents are not detected, they could lead misinformation.
GPT-3
ChatGPT
76.47
76.41
GPT-3
GPT-3
4
73.11
74.69
1SelfCheck w/ unigram (max)
20
64.71
64.91
SSeffCheck w/ NLI
20
74.14
73.78
Acknowledgments
Table 4: Comparison of GPT-3 (text-davinci-O0) and Chat- GPT (gpt-3.5.turbb) the prompt-based text evaluator in SellCheckGPT-Prompt.. TTken from Table 2 for comparison.
This work is supported by Cambridge University Press & Assessment (CUP&A), a department of The Chancellor, Masters, and Scholars of the Uni- versity of Cambridge, and the Cambridge Com- monwealth, European & International Trust. We would like to thank the anonymous reviewers for their helpful comments.
The results in Table 4 show that GPT-3 can self- check its own text, and is better than the unigram method even when using only samples. However, ChatGPT shows a slight improvement over GPT-3 in evaluating whether the sentence supported by the context. More detaily are in Appendix C.

---

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---

A Models and Implementation
unanswerable questions which have a lower than threshold. Next, we derive how Bayes' theorem can be applied to take into account the number of answerrble/nnanswerable questions.
A.1 Entropy
The entropy of the output distribution is imple-
mented as follows,
B.1 SellCheckGPT-QA with Bayes
Hij (t)) lo82 (1)(16)
(13)
Let P(F) denote the probability of the i-th sentence being non-factual, and P(T) denote the probability of the i-th sentence being factual. For question g the probability of i-th sentence being non-factual given set of matched answers Ln and set of not-matched answers In is:
where W is the set of all possible words in the vocabulary.
A.2 Proxy LLMs
The proxy LLMs considered are LLaMA-(7B 13B, 30B) (Touvron et al., 2023), OPT- (125m, 1.3B, 13B, 30B} (Zhang et al., 2022), GPT-J-6B (Wang and Komatsuzaki, 2021) and GPT-NeoX- 20B (Black al., 2022).
P(F Lmg Lm)
P(L.: L.a F((F)
P(Ln, L P(F) + P(Lm: Lm P(T)
P( Lm(F)
(16)
A.3 SelfCheckGPT's Systems
P(Ln, Ln(F) + P(Ln. [T
Question Answering: The generation systems and G2 are Large fine-tuned to SQuAD (Rajpurkar et al., 2016) and RACE (Lai et al., 2017), respectively. The answering system A is Longformer (Beltagy et ul., 2020) fine-tuned to the RACE dataset. The answerability system U is also Longformer, but fine-tuned to SQuAD2.0.
where we assume the sentence is equally likely to
be False or True, i.e. P(F) P(T). The probabil- ity of observing Tmn In when the sentence is False (non-factual):
P(Im: Ln(F)
II P(a Fa II P(d' # Fn) ae
d'EL2
LLM for Prompting:
We consider two LLMs, GPT-3 (extt-davinc-0033 and ChatGPT (gpt-3.5- turbo) We note that during the data creation and annotation, GPT-3 (text-davinci-0033 was the state- of-the-art LLM available; hence, GPT-3 was used the main LLM generating WikiBio passages.
(1 - (.)
(17)
and probability of observing Lm, Ln when the sen- tence True (factual):
P(Ln; LT
II P(a (r!T) dEL,
II P(a' aTl
B SelfCheckGPT with QA
e'El2
Previous work showed that implementing question generation (in Equation 2) with two generators (G1 generates the question and associated answer, and G2 generates distractors) yields higher-quality dis- tractors (Manakul et al., 2023). Thus, a two-stage generation is adopted in this work as follows:
Na2 -3)
(18)
where N₂ and N₂ are the number of matched an- swers and the number of not-matched answers, simplify Equation 16:
spectively. Hence,
No
72
Pm Ln)
(19)
a ~ Pai(g, ar::); Dia Pa2(o 4, a, R)
No No
71
72
(14)
where o = fa, foi,... ). In addition, to filter out bad (unanswerable) questions, we define an answerability score (Raina and Gales, 2022):
where 71 = T- and 7/2 = T- Lastly, instead of rejecting samples having an answerability score below threshold, 7 we find empirically that soft- counting (defined below) improves the detection performance. We set both 31 and B3 to 0.8.
= Po Ranasweerrblllgg context)
(15)
where the context either the response R or sam- pled passages S", and a + 0.0 for unanswerable and a - 1.0 for answerable. We use a to filter out
'xz is between 0.0 (unanswerable) and 1.0 (answerable). Standard-counting N. and N, can be considered as a special case of soft-counting where a is set to 1.0 if a is greater than the answerability threshold and otherwise is 0.0.

---

Nº
Σ
Oni No
Σ On (20)
Sent-lvi AUC-PR
Passage-Ivl
I--ram
NoFac
NoFac
Fact
Pear.
Spear.
n. S.t. CnC
s.t l/
Avg( loge)
where On = P S*). Therefore, the SelfCheckGPT with QA score, SQA, is:
1-gram
81.52
40.33
41.76
40.68
39.22
2-gram
82.94
44.38
52.81
58.84
58.11
3-gram
83.56
44.64
53.99
62.21
63,00
4-gram
83.80
43.55
54,25
61.98
63.64
N
72
5-gram
83.45
42.31
53.98
60.68
62.96
SQA La [m)
(21)
No
No
Max(-loguy
72
I-gram
85.63
41.04
58,47
64.71
64.91
2-gram
85.26
39.29
58.29
62.48
66.04
In Table 5, we show empically that applying Bayes' theorem and soft counting (in Equation 20) im- proves the performance of the SelfCheckGPT with
3-gram 84.97
37.10
$7.08
57.34
60.49
4-gram 84.49
36.37
55,96
55.77
57.25
5-gram 84.12
36.19
54.89
54.84
55.97
QA method.
Table 7: The performance using different n-gram models SelfCheckGPT with w-gram method.
Sentence-lvi NoF
Passage-Ivl
Varaint
NoF
Fact
PCC
SCC
SimpleCount
83.97
40.07
47.78
57.39
55.15
+ Bayes
83.04
38.58
47.41
56.43
55.03
+ Bayes +
84.26
40.06
48.14
61.07
59.29
925
Table
Performance of SelfCheckGPT-QA's variants.
40.0
87.5
C SelFCheckGPT with Prompt
AUC-P
We use the prompt template provided the main text (in Section 5.5) for both GPT-3 (text-davinci- 003) and ChatGPT (gpt-3.5-turbo). For ChatGPT, a standard system message "You are helpful assistant. is used in setting up the system. At the time of conducting experiments, the API costs per 1,000 tokens $0.020 for GPT-3 and $0.002 for ChatGPT. The estimated costs for run- ning the models to answer Yes/No on all 1908 sen- tences and samples are around $200 for GPT-3 and $20 for ChatGPT. Given the cost, we conduct the experiments on samples when performing the ablation about LLM choice for SeIfCheckGPT- Prompt (Section 7.3). Table 6 shows the breakdown of predictions made by GPT-3 and ChatGPT.
Seltk PERTRBERRTRR Scare
80.0
SeifCc
SwnCk-ungran
SenCk NLI Se SenCa Prompt
175
17 Numn samples
10
14
15
18
20
Figure 8: The performance of SeifCheckGPT methods sentence-level non-factual detection (AUC-PR) versus the number of samples. This Figure extends the passage-level results in Figure 7.
40
30
Spearman
ChatGPT
GPT-3
Yes
3179
1038
No
367
3048
-10
LLaMM
NeT
Table Breakdown of predictions by GPT-//hhaGPT when prompted to answer VestsupportedyNoooottieoo supporred)
208
Model
D Additional Experimental Results
Figure 9: Passage-level ranking performance of the Avg(7()
method using proxy LLM where the sizes are: LLaMA=(7B. 13B, 30B). OPT=(125m, 1.3B, 13B, 30B). GPT-J-6B, NeoX=20B. The full results are provided in Table 8.
Here, we provide experimental results that are com- plementary to those presented the main paper.

---

Matrod Srow
16
0A4
3.5
(a) SeffChecKGT--BRRTScoom
(b) SelfCheckGPT-QA
(c) SeltCheckGPP-I (mamm
(d) SelfCheckGPT-NL.
Figure 10: Scatter plat of passage-level scores where Y-axis = Method scores, X-axis = Human scores. Correlations are reported in Table 2. This figure provides results in addition to Figure 6.
Sentence-level (AUC-PR) NonFact*
Passage-level (Corr.)
L.M
Size
NonFact
Factual
Pearson
Spearman
Random
72.96
29.72
27.04
Avg(-logp) Method
LLaMA
75.43
30.32
41.29
20.20
13B
74.16
30.01
37.36
13.33
12.89
LLaMA
7B
71.69
31.30
-2.59
OPT
30B
67.70
24.43
-32.07
-31.45
NeoX
69.00
24.38
-31.79
-34.15
OPT
13B
67.46
24.39
25.20
-33.05
-32.79
GPT-J
6B
67.51
24.28
24.26
38.80
-00.55
OPT
1.3B
66.19
24.47
23.47
33.20
-38.95
OPT
125m
66.63
25.31
23.07
30.38
-37.54
Avg(H) Method
LLaMA
30B
80.80
39.01
42.97
33.80
39.49
LLaMA
13B
80.63
38.98
40.59
29.43
33.12
LLaMA
7B
78.67
37.22
33.81
19.44
21.79
OPT
30B
77.13
33.67
29.55
-0.43
3.43
NeoX
20B
77.40
32.78
30.13
5.41
7.43
OPT
13B
76.93
33.71
29.68
0.25
1.39
GPT-J
6B
76.15
33.29
28.30
-2.50
-1.37
OPT
1.3B
74.05
31.91
26.33
10.59
-10.00
OPT
125m
71.51
30.88
25.36
14.16
-13.76
Max(
logp) Method
LLaMA
30B
74.01
27.14
31,08
-2,883
-2.711
LLaMA
13B
71.12
26.78
28.82
-34.93
-31.70
LLaMA
7B
69.57
25.91
26.54
-42.57
-38.24
OPT
30B
67.32
24.40
24.32
-49.51
-45.50
NeoX
20B
67.51
23.88
24.82
-47.96
-44.54
OPT
13B
67.36
24.67
24.46
50.15
-4.42
GPT-J
6B
67.58
23.94
23.93
-51.23
-47.68
OPT
1.3B
68.16
25,85
24.66
45.60
-42.39
OPT
125m
69.23
27.66
24.14
-3.22
-37.18
Max(H) Method
LLaMA
30B
80.92
37.32
37,90
35.57
38.94
LLaMA
13B
80.98
37.94
36.01
32.07
34.01
LLaMA
7B
79.65
35.57
31.32
22.10
22.53
OPT
30B
76.58
33.44
29.31
1.63
6.41
NeoX
76.98
31,96
29.13
OPT
13B
76.26
32.81
29.25
1.42
2.82
GPT-J
6B
75.30
32.51
28.13
1.41
OPT
1.3B
73.79
26.38
-9.80
71.32
31.65
25.36
-18.05
-17.37
Table AUC-PR Detecting Factual Factual Sentences the GPT-3 generated WikiBio passages. Passage-level PCC and SCC with L.Mss used GPT-3 responses. This table extension Table 2.


Reviews:
[{"review_id": "K7CPFVad78", "reviewer": "Reviewer_3S1N", "summary": "", "questions": "1. I couldn't locate any explanation regarding the random baseline. After reviewing the code, I noticed that it's simply based on calculating the proportion of positive samples, rather than a random 0-1 label assigned to each sentence. I wonder is it appropriate to call it \"random\"?\n2. For the non-factual* setting, the minor-inaccurate samples are all categorized as negative ones (label 0), right?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "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 Reviewer 3S1N for your helpful feedback and questions. We hope that our responses below address/answer your comments:\n\n**Question 1**: What we mean by “random baseline” is that the baseline randomly predicts the positive class (e.g., non-factual) with probability $p$ (and negative class with prob. $1-p$) on any sentence. By doing this, the expected precision = the proportion of the positive examples, while recall = $p$. Thus, when sweeping $p$ from 0.0 to 1.0, it gives a horizontal line in Figure 5 and therefore AUC-PR = the proportion of the positive examples. More explanation of the random baseline can be found in this StackOverflow article: https://stats.stackexchange.com/a/266989.\n\n**Question 2**: Yes, in the non-factual* setup, minor-inaccurate samples are assigned the negative label (0).\n\n**Reason To Reject 1**: We agree that Selfcheck-Prompt can be computationally heavy (as noted in the limitations). However, we believe the main contribution of our work is demonstrating an effective and simple solution for black-box zero-resource LLM hallucination, which has been underexplored and with no established solutions. In this paper, we have not focused on computational efficiency, though we note that there are simple ways to improve efficiency, for example through distillation. Alternatively, following experiments that use a small deberta-v3 classifier (with 435M params) trained on MNLI to predict entailment/contradiction yield competitive performance (shown in the table below), and require only $N$ API calls for obtaining samples. Further, one could condition the stochastic samples on the main response, which could reduce the number of required samples. Nonetheless, we believe that the main contribution of the paper is its novel idea and demonstrated experimental results, which we believe may guide future research in LLM hallucination, where the method can be refined to be more effective as well as efficient. \n\n| Method                   |  NonFact (AUC-PR)  |  Factual (AUC-PR)  |   Ranking (PCC)   |\n|-----------------------------  |:------------------:|:------------------:|:-----------------:|\n| SelfCheck-NLI             |        92.50       |        66.08       |       74.14       |\n| SelfCheck-Prompt       |        93.42       |        67.09       |       78.32       |\n\n\n**Reasons to Reject 3**: We agree that our method was shown to be effective for the particular task setup. Although the current version of the work may not be applicable to all scenarios, as mentioned in our previous response, there are simple extensions that could be applied to increase the generalisability and effectiveness of the approach. For example, one could generate samples conditioned on the original response so that one can align stochastic samples with information to be checked, e.g., if for the prompt `introduce a sports celebrity to me`, the system generates the response`Lionel Messi is …`, one can then dynamically prompt the LLM to generate facts/information about `Lionel Messi` and apply selfcheck in the provided scenario (in an automated fashion).", "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": "1. This paper provides a strong baseline for the emerging field of LLM hallucination detection, offering insights for future related research.\n2. The proposed method exclusively relies on LLM responses, eliminating the necessity for supplementary knowledge or access to LLM's internal states.\n3. Each of the four variants of SelfCheckGPT demonstrates impressive performance gains in contrast to the baselines, across both sentence-level and passage-level evaluations.\n4. The author released WikiBio GPT-3 dataset comprising 238 passages and 1908 annotated sentences. This resource stands to facilitate researchers in delving into the intricacies of LLM hallucination.\n5. The experiment section is meticulously designed, providing strong support for the author's insights.\n6. This paper is well written.", "reasons_to_reject": "1. The method presented relies on extracting multiple responses from the LLM. For the variant with optimal performance, LLM prompting, 20 samples are needed to achieve the best reported results. Assuming a response contains 5 sentences, this requires 100 API calls to obtain a passage-level score (if I understand correctly), which is cost heavy and ineffective.\n2. It remains unclear whether the proposed approach is suitable for detecting hallucinations in responses from other LLMs and across various application scenarios beyond WikiBio. This uncertainty arises because the experiment dataset exclusively encompasses WikiBio responses drawn from text-davinci-003.\n3. The proposed method might struggle to detect hallucinations in open-ended responses, for example, the prompt \"introduce a sports celebrity to me\". In this case, the sampled responses could pertain to different individuals, making it challenging to identify shared information for consistency checking.", "questions_to_authors": "1. I couldn't locate any explanation regarding the random baseline. After reviewing the code, I noticed that it's simply based on calculating the proportion of positive samples, rather than a random 0-1 label assigned to each sentence. I wonder is it appropriate to call it \"random\"?\n2. For the non-factual* setting, the minor-inaccurate samples are all categorized as negative ones (label 0), right?", "paper_topic_and_main_contributions": "This paper proposed SelfCheckGPT, a black-box, zero-resource method for LLM hallucination detection. This approach utilizes multiple sampled responses to verify the information consistency. The primary contributions include:\n1. A zero-resource method for detecting hallucinations in LLM responses, alongside four SelfCheckGPT variations: BERTScore, question-answering, n-gram, and LLM prompting.\n2. The release of an annotated dataset for sentence-level hallucination detection in LLM.\n3. The proposed method achieves notable results in comparison to baseline approaches, all without requiring supplementary knowledge bases or token-level probabilities.", "excitement": 4, "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": {}, "extra_text": {}}, {"review_id": "AakAsgxdic", "reviewer": "Reviewer_91B1", "summary": "", "questions": "Question A: as the example in Figure 1 shows, pronouns are quite commonly involved in these descriptive paragraphs, how is coreference resolution handled in your approach, regarding the sentences taken from LLM generation, potentially containing isolated pronouns, and the sampled reference text?\n\n", "limitations": "", "rating": 3, "confidence": 2, "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 Reviewer 91B1 for your useful feedback, queries and suggestions. We hope that our responses below are helpful in clarifying your questions and points:\n\n**Question A**: We do not explicitly handle coreference resolution, but since generated articles typically have one main topic and entire stochastic passages are used in selfcheck, resolving pronouns is likely to be simple for the approach to implicitly do (as observed with our strong results). However, coreference resolution is an interesting suggestion and would be a useful tool to incorporate in future applications of the method, particularly when responses have multiple persons/objects. \n\n**Reason To Reject 1**: We agree that Selfcheck-Prompt can be computationally heavy (as noted in the limitations). However, we believe the main contribution of our work is demonstrating an effective and simple solution for black-box zero-resource LLM hallucination, which has been underexplored and with no established solutions. In this paper, we have not focused on computational efficiency, though we note that there are simple ways to improve efficiency, for example through distillation. Alternatively, following experiments that use a small deberta-v3 classifier (with 435M params) trained on MNLI to predict entailment/contradiction yield competitive performance (shown in the table below), and require only $N$ API calls. Further, one could condition the stochastic samples on the main response, which could reduce the number of required samples. Nonetheless, we believe that the main contribution of the paper is its novel idea and demonstrated experimental results, which we believe may guide future research in LLM hallucination, where the method can be refined to be more effective as well as efficient. \n\n| Method                   |  NonFact (AUC-PR)  |  Factual (AUC-PR)  |   Ranking (PCC)   |\n|-----------------------------  |:------------------:|:------------------:|:-----------------:|\n| SelfCheck-NLI             |        92.50       |        66.08       |       74.14       |\n| SelfCheck-Prompt       |        93.42       |        67.09       |       78.32       |\n\n*We addressed a similar comment from Reviewer 3S1N which this response was based on.\n\n**Reason To Reject 2**: We would argue that LLMs are widely used by developers (who are not the LLM providers) who do not have access to the hidden states of LLMs like GPT3. Therefore, integrating hallucination detection into their applications requires a black-box approach. Secondly, we selected GPT3 for our investigation as it is widely used in both industry and academia. This system is considered to be much more capable at text generation than existing open-source LLMs, which was especially true when our experiments were conducted (when Llama2 and Falcon did not exist). We note that although our methods work for black-box systems, black-box solutions are the most general and are applicable to grey/white-box systems, and our results demonstrate that it can outperform grey-box baselines. \n\n**Reason To Reject 3**: Our results show that there is poor alignment between the proxy LLM and the system probabilities, which implies that it is unlikely that the proxy hidden representations would align well. Furthermore, the methods in Azaria and Mitchell, 2023 are white-box and supervised approaches, which require training data to train a classifier using the hidden representations, limiting the robustness and generalizability of the method. \n\n**Reason To Reject 4**: What we meant by “the first work to analyze model hallucination of general LLM responses” is that although previous works have looked at identifying hallucination in NLG tasks (e.g. summarization), none consider hallucination detection of LLM responses when LLMs are probed to freely generate responses to probed queries. We agree that our wording here can be confusing, and we will make changes in future versions to make this statement clearer.", "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 well-written, related work is well-discussed, and experiments and ablation studies are thorough.", "reasons_to_reject": "The weaknesses in this paper is primarily three-fold: \n\n1) the proposed approach involves at least the re-generation of N samples with an LLM for each candidate generation to be verified, this comes at a high computational cost; for the prompt-based inconsistency measure, a further N*I number of queries are necessary;\n\n2) the paper assumes the unavailability of the hidden states and output log probs when attempting to verify the factuality of LLM outputs; however, the likelihood of this assumed condition is not well-justified: for LLM service providers who have the responsibility to verify the generations, they would have access to the inner parameters; for the research community, it is ultimately more sound to make use of open-source LLMs such as LLaMA-2 / Falcon etc. in favor of such commercial products as ChatGPT;\n\n3) with the black-box assumption, the authors excluded what they called \"white-box solutions\" from the comparison, such as (Azaria and Mitchell, 2023); however, it looks as though the white-box solutions are actually possible for the assumed setup, a similar way as the Proxy LLMs baseline. In fact, this would also be similar to the way the (Azaria and Mitchell) reference elicited their dataset in some domains.\n\nAnother weakness is, the paper claims to be the \"first work to analyze model hallucination of general LLM responses\", however their experiments are conducted on a rather restricted domain of WikiBio, with mostly factoid generations.\n", "questions_to_authors": "Question A: as the example in Figure 1 shows, pronouns are quite commonly involved in these descriptive paragraphs, how is coreference resolution handled in your approach, regarding the sentences taken from LLM generation, potentially containing isolated pronouns, and the sampled reference text?\n\n", "paper_topic_and_main_contributions": "The paper introduces a method to verify the factuality of sentences and paragraphs generated by LLMs, based on other utterances sampled from the same LLM for the same queries. The paper experimented with a few metrics for inconsistency, including BERT, a QA model, n-gram frequencies, and prompting an LLM. On a dataset they created based on WikiBio, their method demonstrated promising results compared with other \"black box\" or \"grey box\" approaches.", "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": {"Missing_References": "No missing reference is noticed, the paper is well-situated in its context.", "Typos_Grammar_Style_and_Presentation_Improvements": "None detected."}, "extra_text": {}}, {"review_id": "n28cmDtMmP", "reviewer": "Reviewer_JhFY", "summary": "", "questions": "1. What are the other sampling parameters for the stochastically generated samples? For instance, say if the generated samples focus too much on diversity, it might become too inconsistent and adversely affect performance. Have you tried different sampling parameters?\n2. Why take average for aggregation in section 7.2 instead of min/max? If a sentence in a passage is non-factual, it should indicate the entire passage contains non-factual info and thus the passage should be non-factual.", "limitations": "", "rating": 4, "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 Reviewer JhFY for your useful feedback, queries and suggestions. We hope that our responses below are helpful in clarifying your questions and points:\n\n**Question 1**: A sampling parameter that may influence the responses is the generation temperature. In this work, we use the standard value and set the temperature to 1.0. If a temperature of 0.0 is used, then there is no diversity and self-check would not work. Similarly, for a high temperature, the generated samples would be highly diverse and even somewhat random, which can be unsuitable for generating evidence for self-check. With this insight, we hypothesize that there is a range of temperature values for which the selfcheck method can operate effectively. The paper selected the default value of temperature of 1.0 which provided effective performance, however, an interesting future ablation would be to plot detection performance against temperature.\n\n\n**Question 2**: We believe that taking the mean for aggregation is suitable because we want to capture the granularity of how factual a document/passage may be. Using a min or max operation for the document-level labels would effectively set binary labels of either non-factual or factual, which would lose fine-grained information about how factual the document is on a more fine-grained scale, which the mean operation provides.\n\n**Reason(s) to reject**: We agree that there are similarities between LLM hallucination detection and fact-checking, however, LLM hallucination aims to identify if an LLM has hallucinated, while fact verification aims to identify if a fact is true with respect to world knowledge. For fact-checking, the texts have already been created by humans or other automatic systems and are *not* generated from the LLM. This paper focuses specifically on LLM hallucination detection, and though it may be interesting to examine how the methods operate on fact verification tasks, that is not the aim of the paper. As a result, we had to curate a new evaluation dataset specifically for assessing LLM hallucination, and to the best of our knowledge, no other dataset for *real* LLM hallucination detection existed prior to our experiments.", "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": "Checking LLM hallucinations is a very important topic, this paper proposes to use LLM to self-check (or external LLMs to assist). The authors also curated a fact-checking dataset that is used to show the effectiveness of the proposed method.", "reasons_to_reject": "Although the curated dataset may be an asset to the community specifically for LLM hallucination/factuality, the evaluation is slightly weak as it was the only evaluated dataset, when multiple fact-checking datasets have commonly been used by the automatic fact-checking community (see Guo et al., 2022).\n\nGuo et al. 2022: A Survey on Automated Fact-Checking, TACL", "questions_to_authors": "1. What are the other sampling parameters for the stochastically generated samples? For instance, say if the generated samples focus too much on diversity, it might become too inconsistent and adversely affect performance. Have you tried different sampling parameters?\n2. Why take average for aggregation in section 7.2 instead of min/max? If a sentence in a passage is non-factual, it should indicate the entire passage contains non-factual info and thus the passage should be non-factual.", "paper_topic_and_main_contributions": "This paper investigates using LLMs to self-check their outputs for non-factual statements without resorting to fact-checking against reference data nor having access to output probability distributions. The authors propose 4 different methods (BERT score, QA, n-gram, prompting) of using an LLM to check the consistency of the generated output at the sentence level, which are then aggregated. The WikiBio dataset was annotated in this work at the sentence level into three factuality classes for evaluation, and experiments with GPT-3 show improvements over baselines, including grey-box approaches, with analysis on using different LLMs and comparison against fact-checking with external knowledge. The main contributions of this paper are:\n1. Proposed using LLMs to self-check for factuality, which is black-box and doesn't use external resource.\n2. Information consistency is checked on the sentence level, with 4 variants of measuring consistency proposed.\n3. Curated a dataset based on WikiBio for evaluating hallucination.\n4. Showed on GPT-3 generated text that the proposed method can outperform baselines on the curated dataset.", "excitement": 4, "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": {}, "extra_text": {}}, {"review_id": "F1rI7VPStU", "reviewer": "Reviewer_XVG4", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "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 Reviewer XVG4 for your useful feedback and suggestions, and thank you for the relevant reference, which we will incorporate into the revised version of this paper.", "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": "- Point out an important yet under-evaluated task, hallucination detection.\n- The methodology the authors propose seems intuitive, but the contribution of the dataset and associated experiments are more benefitial to the community.\n- Experiments are solid, covering all the basis including the role of external knowledge.", "reasons_to_reject": "- One potential reasons to reject is the limited scale and coverage of the dataset. But I believe it's a good inspiration to the community as a pilot work.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper introduces a zero-resource hallucination detection method based on self-consistency of LLMs. They propose to evaluate stochastically-generated responses by LLMs and compare them with four similarity evaluation metrics. As no propoer evaluation sets exist, another major contribution of this paper is the presense of a sentence-level annotated hallucination detection dataset, consisting of 1908 sentences. Extensive experiments show the effectiveness of the proposed model.", "excitement": 4, "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": {"Missing_References": "A self-consistency driven prompting method for LLM that is relevant to your method.\n\nXuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171."}}]

Author rebuttal:

Meta-review: This paper investigates hallucination detection from LLMs, introducing a formal version of the task and creating strong baselines. This will encourage future work both through methods and through the provided dataset. The only noteworthy negatives identified in the review process were some assumptions of what is required from the underlying LLMs. However, these assumptions do not seem impossible for most models.
Decision: Accept-Main