FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Authors: Sewon Min
Kalpesh Krishna
Xinxi Lyu
Mike Lewis
Wen-tau Yih
Pang Wei Koh
Mohit Iyyer
Luke Zettlemoyer
Hannaneh Hajishirzi
Venue: EMNLP 2023 Main (2023)
Keywords: Text Generation
Factuality
Evaluation
Large Language Models
TL;DR: We introduce FActScore, a new evaluation for factual precision in long-form generation.

Abstract: Evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial because (1) generations often contain a mixture of supported and unsupported pieces of information, making binary judgments of quality inadequate, and (2) human evaluation is time-consuming and costly. In this paper, we introduce FACTSCORE, a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atomic facts supported by a reliable knowledge source. We conduct an extensive human evaluation to obtain FACTSCOREs of people biographies generated by several state-of-the-art commercial LMs—InstructGPT, ChatGPT, and the retrieval-augmented PerplexityAI—and report new analysis demonstrating the need for such a fine-grained score (e.g., ChatGPT only achieves 58%). Since human evaluation is costly, we also introduce an automated model that estimates FACTSCORE using retrieval and a strong language model, with less than a 2% error rate. Finally, we use this automated metric to evaluate 6,500 generations from a new set of 13 recent LMs that would have cost $26K if evaluated by humans, with various findings: GPT-4 and ChatGPT are more factual than public models, and Vicuna and Alpaca are some of the best public models. FACTSCORE is available for public use via `pip install factscore`.

FACTSCORE: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Xinxi Lyu' Mike Lewis* Wen-tau Yih Pang Wei Koh' Mohit Iyyer? Luke Zettlemoyer! Hannaneh Hajishirzi!.
Sewon Min Kalpesh Krishna 1
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Abstract
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Evaluating the factuality of long-form text gen- erated by large language models (LMs) is non- trivial because (1) generations often contain mixture of supported and unsupported pieces of information, making binary judgments of quality inadequate, and (2) human evaluation is time-consuming and costly. In this paper, we introduce FACTSCORE, new evaluation that breaks generation into series of atomic facts and computes the percentage of atomic facts supported by reliable knowledge source. We conduct extensive human evaluation ob- lain SCCTSCORES of people biographies gen- erated by several state-of-the-ee commercial LMs InstruCTt ChatGPT, and the retrieval- augmented PerplexityAI--ane report new anal- ysis demonstrating the need for such fine- grained score (e.g.. ChatGPT only achieves 58%). Since human evaluation costly, we also introduce automated model that esti- mates FACTSCORE using retrieval and strong language model, with less than a 2% error rate. Finally, use this automated metric to eval- uate 6,500 generations from a new set of 13 recent LMs that would have cost $26K if evalu- ated by humans, with various findings: GPT-4 and ChatGPT are more factual than public mod- els, and Vicuna and Alpaca public models. FACTSCORE is available for public use via pip install factscore.'
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Figure 1: An overview of FACTSCORE, a fraction of alomic facts (pieces of information) supported by given knowledge source. FACTSCORE allows a more fine-grained evaluation factual precision, e.g., in the figure, the top model gets score of 66.7% and the bottom model gets 10.0%, whereas prior work would assign 0.0 both. FACTŠCORE can either be based on human evaluation, or he automated, which allows evaluation of a large set of LMs with no human efforts.
mation that are mixture of true or false, making a binary judgment inadequate (Pagnoni et al., 2021). Sccond, validating every piece of information is time-consuming and costly.
of the hest
In this paper, we introduce FACTSCORE (Factual precision in Atomicity Score), eval- uation of an LM that represents the percentage of atomic facts (pieces of information) supported by a given knowledge source. Computing FACTSCORE involves (1) breaking a generation into a series atomic facts-short statements that each contain one piece of information (Nenkova and Passonneau, 2004; Shapira et al., 2019; Zhang and Bansal, 2021; Liu et al., 2022), and (2) assigning binary label
1 Introduction
Long-form text generated by large language mod- els (LMM) has widely been used (Brown et al., 2020; Ouyang et al., 2022); nonetheless, evaluating their factual citeiiiion-wethie each piece of informa- tion conveyed in generation is factually accurate- remains challenging for two reasons. First, a gener- ation consists of a large number of pieces of infor-
Coore contributors.
EEenn a single sentence consists of multiple pieces of information (e.g., 4.4 per sentence in ChaiGPT, 40% of which are a mixture of supported and unsupported information).
SSouree code and guidelines are available at https:// gi thub. com https/hhhw////mmmm FcttS/or.e....ee.eeem

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each atomic fact, allowing fine-grained evalua- tion of factual precision. We evaluate FACTSCORE on the task of generating people biographies be- cause generations consist of verifiable statements rather than debatable or subjective ones, and the scope is broad (i.c., covering diverse nationalities, professions, and levels of rarity).
extend FACTSCORE for broader of gen- crations (e.g.. open-ended generation) and further improve the estimator.
2 Related Work
Factual precision in text generation. Factual precision in text generation has been an active area of research in NLP. Most prior work studies fac- tual precision of models supervised for a specific problem such as dialogue (Shuster et al., 2021), or focuses on question answering with short an- swers (Kadavath al., 2022; Kandpal et al., 2022; Mallen et al., 2023; Nori al., 2023).
We perform extensive human annotations ob- tain FACTSCORES of three state-of-the-arts com- mercially available LMs: InstructGPT (Ouyang et al., 2022), ChatGPT (OpenAI, 2022), and search- augmented PerplexityA1. Our results indicate that commercially available LMs are riddled with er- rors, having FACTSCORES of 42%, 58% and 71 71% respectively. Their FACTSCORES significantly drop as the rarity of the entities increases, e.g..
More recent work has studied factual precision of text generation beyond short answers. Lee al. (2022) evaluates the factual precision with proxy metrics, e.g., whether named entities in a gener- ation appear in an article of the topic. A series of concurrent work verifies the precision of the ci- tations (attributions) provided by the model (Gao et al., 2022; Liu et al., 2023a; Yue et al., 2023; Gao et al., 2023). A concurrent work by Manakul et al. (2023) automates the identification of fac- tual errors in LM generations without using any knowledge source; we use their method as a base- line estimator Section 4. In contrast, our work (1) considers much longer text generation" from a variety of state-of-the-art LMs with and without search, (2) provides their fine-grained evaluation both by human experts and through an automated evaluator that closely approaches humans, and (3) applies it to large set of LMs at scale.
80% + 16% for ChatGPT.
Since human evaluation is costly, we next in- troduce an automatic evaluation of FACTSCORE through a model that estimates FACTSCORE for a given LM. Our estimator decomposes genera- tions into atomic facts and validates each based a given knowledge source, leveraging retrieval from the given knowledge source and strong lan- guage models. Our estimator closely approximates FACTSCORE with an error rate of < 2% and can be applied to a range of new LMs at scale with no human effort. Our case study evaluates 6,500 gen- erations from 13 LMs that could have cost $26K, with various findings: GPT-4 (OpenAI, 2023) and ChatGPT are far less factual than humans but much better than public models, and there is large variance between public models, with Vicuna (Chi- ang et al., 2023) and Alpaca (Taori et al., 2023) being some the best.
Fact Verification. Our work is closely related to prior work on fact verification (Thomn et al., 2018; Wadden et al., 2020) where claim sentences are automatically checked against a large knowledge source like Wikipedia or scientific literature. Most literature assumes a single, atomic claim, some- times modeled with surrounding context (Nakov et al., 2018; Mihaylova et al., 2019; Shaar et al., 2022). There also has been work that verifies a longer sentence or text through decomposition to atomic facts (Fan et al., 2020; Wright al., 2022; Chen al., 2022; Kamoi et al., 2023) from which we take inspiration. The primary difference be- tween fact verification literature and our work is that we focus on long-form model- rather than sentence-level model-generant generated text claims.
In summary, our contributions are as follows.
1. We introduce FACTSCORE, a new evaluation of factual precision of LMs by breaking their gen- erations into atomic facts and validating each against given knowledge source. Human eval- uation reveals that the state-of-the-art LMs with and without search have low FACTSCORES.
2. We introduce a model that approximates FACTSCORR with an error rate of < 2%, al- lowing evaluation of a large set of new LMs without manual human efforts.
3. We open- sourced FACTSCORE and the anno- tated data for public use, available via pip install factscore. We suggest future work
CConsisting of 110-151 words (Table 1), in contrast to 18-29 in Gao et al. (2022) and 65 in Liu et al. (2023a).
pperplexity.ai

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Model-based Evaluation. Prior work has used learned models define automated evaluation scores (Zhang et al., 2020; Liu al., 2023b). This includes model-based evaluation in summarization that considers the consistency between a summary and source document using QA or NLI (Kryscin- ski et al., 2020; Wang et al., 2020; Fabbri et al., 2022; Deutsch et al., 2021; Laban et al., 2022). We take inspiration from this work, and evaluate fac- tual precision of LM generations by considering whether pieces of information are supported by a large text corpus.
ing the first generation higher than the second. Key Idea Factual precision as function of a given knowledge source. Prior work often consid- ers factual precision as a single global truth (Man- akul et al., 2023). In contrast, we adopt a per- spective that the truthfulness of a statement should depend on a particular knowledge source that end users consider to be trustworthy and reliable. There- fore, instead of whether an atomic fact is globally true or false, we consider whether is supported by a given source of knowledge. This has been used the fact verification literature (Wadden et al., 2022) where conflict of information between different sources is relatively common.
3 FACTSCORE: Evaluating Factual Precision of Long-form Text Generation
Definition. Let M be language model to be eval- uated, A' be set of prompts, and C be knowledge source. Consider response y Mr for C X and Ay. list of atomic facts y. FACTSCORE of M is defined as follows.
We introduce FACTSCORE, new evaluation of an LM that considers the factual precision of atomic facts generated by the LM. We perform human evaluations to calculate FACTSCORES of the state- of-the-art LMs (Section 3.3) and discuss results (Section 3.4). FACTSCORE allows rigorous and fine-grained evaluation of factual precision, but is time-consuming and costly, motivating automatic evaluation in Section 4.
((v) = [ Σ I[a is supported by C]. dEA,
(ACTSCORE(M) = [f responds).
Mr responds means M did not abstain from sponding the prompt x. This definition assumes the following:
3.1 Definition
FACTSCORE is based on two key ideas.
Atomic fact as unit. Long-form text consists of many pieces of information that can each be either true false. Prior work has explored using a sentence sentence is a mix of supported and unsupported facts, e.g., 40% of the cases with ChatGPT. Pre- vious and concurrent work either (1) defines an additional label of partial support (Manakul et al., 2023; Liu et al., 2023a) whose definition may be subjective and can lead to low agreement, (2) takes the strictest definition of suppor't that requires every piece of information to be sup- ported (Rashkin et al., 2021; Gao et al., 2022), which ignores the partial support cases, e-g., as- signing 0.0 to both generations in Figure even though the first generation is considerably more accurate than the second.
Key idea 1:
Whether or not an atomic fact supported by C is undebatable.
2. Every atomic fact Ag has an equal weight of importance, following Krishna et al. (2023).
unit; however, even single
Pieces of information in do not conflict or overlap with each other.
In the rest of the paper, we propose to use people biographies as X' and Wikipedia as C because they satisfy these assumptions to reasonable degree (Section 3.3). We discuss in which cases these assumptions hold may not hold in more detail in the Limitation section.
FACTSCORE considers precision but not recall, e.g., model that abstains from answering too often or generates text with fewer facts may have a higher FACT SCORE, even if these are not desired. We leave the evaluation of factual recall for future work (more discussion in the Limitation section).
In this paper, we define an atomic fact as short sentence conveying
piece of information (ex- amples in Figure 1), similar summarization con- tent units (Nenkova and Passonneau, 2004). An atomic fact is more fundamental unit than a sen- tence for a piece of information and provides a more fine- -rained evaluation, e.g., in Figure 1, rat-
3.2 Studied LMs
We evaluate three LMs (referred to as LM an LM as a subject): (1) InstructGPT (text-davinci-oo0) updated from Ouyang et al.

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(2022)), (2) ChatGPT (OpenAl, 2022), and (3) PerplexityAI which incorporates a search engine with language model.
InsIGPT
ChatGPT
PPLAI
Use search % responding
99.5
85.8
90.7
# tokens response
110.6
154.5
151.0
3.3 Data
# sentenees response
6.2
7.9
9.8
# facts response
26.3
34.7
40.8
We perform human evaluation of factual precision based definition. We prompt the LMSuBI generate people biographies and evaluate them against Wikipedia for the following reasons.
Statistics the lahels
Supported supporee
42.3
50.0
64.9
43.2
27.5
11.1
Irrelevant
14.0
8.3
14.8
Abstains from answering
0.5
14.2
9.3
Biographies are objective (not subjective or de- batable) and contain specific (not vague) infor- mation, satisfying Assumption in Section 3.1.
FACTSCORE
42.5
58.3
71.5
Table 1: Statistics of the data and FACTSCORE results. InstGPT and PPLAI respectively refer to InstructGPT and PerplexityAL.
Biographies allow evaluation across diverse na- tionalities, professions, and levels of rarities.
responding indicates % of gener- ations that do not abstain from responding. # tokens is based on white space.
Wikipedia offers reasonable coverage of infor- mation about people and is reasonably self- consistent, satisfying Assumption 3.
of the data and calculate the agreement rate: 96%, 90% and 88% for InstructGPT, ChatGPT and Per- plexityAI, respectively. More details are provided in Appendix A.3.
Data collection. We carefully design an anno- tation pipeline assign a factual precision to long-form generation through the following steps.
Step 0: Sampling people entities. We sample 183 people entities from Wikidata who have cor- responding Wikipedia pages. We sample entities annotate from a uniform distribution over cate- gorics defined in Appendix A.I.
3.4 Results
Statistics of the data and results are reported in Table 1.
All LMSuBy's struggle with factual preci- sion errors. InstructGPT and ChatGPT achieve FACTSCORES of 42.5% and 58.3%, respectively. Perplexity AI, which uses commercial search gine and thus should have perfect FACTSCORE if directly copying the text from the correct Wikipedia page, attains FACTSCORE of 71.5%. We provide a qualitative analysis of its error cases in the last paragraph of this section.
Step 1: Obtaining generations. We feed prompt bio of <entity>" the LMSUBJ and take a generation it is. We implement rules to identify generations that abstain from answering and filter them out.
"Tell
Atomic facts generation. Human annota- tors break generation into series of atomic facts. To save annotation time, we provide atomic facts broken down by InstructGPT which human annota- tors can take and revise. Details Appendix A.2.
Step 2:
ChatGPT and PerplexityAT often abstain from answering which presumably improves their fac- tual precision. InstructGPT rarely abstains from answering, likely because it is not trained do so, Irrelevant facts either (a) have dependencies on previous facts generation that turn out to be un- supported, or (b) are irrelevant to the prompt inde- pendent from other facts in generation (examples in Appendix A.4). We find that (b) rarely happens with InstructGPT and ChatGPT but happens con- siderably with PerplexityAl, A1, because PerplexityAl often directly copies search results even if they are largely irrelevant to the input prompt. This is in agreement with a concurrent work from Liu et al. (2023a) that shows generative search engines like Perplexity AI copy incorrect search results and gen- erate text that is irrelevant to the input query.
Step 3: Labeling factual precision & editing. We ask another set of human annotators assign each atomic fact one of three labels. If the atomic fact is clearly not related to the prompt, and thus should be removed from the bio without a validation step, they assign Irrelevant. the fact is relevant, they validate the fact based on the English Wikipedia, and label either Supported or Not Not-supported
We recruit freelancers through Upwork and pay 15-25 USD per hour. Annotation requires exten- sive effort and time, leading the cost of $4 per generation. We assign two freelancers for the 10%
'See Appendix A.5 for a related analysis.

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Category
Example
Single-sentence
33.3 Gen On November 25th, 2023, Glover Teixeira became American citizen. Wiki In November 2020, Teixeira became an American citizen. Gen [Eric Hacker] was named the International Lengue Pitcher Year. Wiki [Eric Hacker] was named the IL Pitcher of the Week.
tradiction (words)
Single-sentence con-
10.0 Gen William Waldegrave's grandfather was James II and VII. wiki His father's title was created for diplomat and ambassador James Waldegrave, 1st Earl Waldegrave, whose grandfather was James and VII. Gen She has appeared in several successful films such as (...) and Zero (2018). Wiki: Zero was commercial failure.
tradiction (beyond words)
Page-level contradic- lion
23.3 Gen Some of [Julia Faye's notable films include "Cleopatra" (1934). Comment No mention of Cleeputra the Julia Faye page, and no mention of Julia Faye on the Cleopotra page. Gen [Kang Ji-hwan] has donated money to various charities and organizations over the years. Comment No such mention on Kang Ji-hwan page.
Subjective
16.7 Gen His uchievements, as an actor and as cultural force, will surely prove to be as heroic as those of the churacters he portrayed. Wiki Culture writer Steve Rose, in The Guardian, wrote: "Chadwick Boseman began career playing African American icons and pioneers; he ends as one himself. His [... achievements, and cultural force, will surely prove to be as heroic as those of the characters he portrayed."
Fact is irrelevant
3.3 Gen [Zamfir Arbore]'s life not well-documented, and there little information available about him.
Wiki is inconsistent
3.3 Gen Kick (2014) that brought [Saji Nadiadwalal various debutant director awards. Wiki 2015, IFA Award for Debut Director, Kick. (..) Kick brought him various debutant director awards. Conment
wrong
text from
lahle that indicates he won award (accurate). The second is inaccurate, incoreetly citing news article.
Annotation ervor
10.0 Gen [Zamfir Arbore] was part of the staff of Românul. Miki The Românul staff came to include Zamfir Arbore. Zamfir Arbore page.
Comment Mentioned the Romdnul page but
Table 2: Categorization of precision errors prot-supported from PerplexityAl (Section A.5). Gen indicates the generation from PerplexityAI, and Wiki indicates evidence text from Wikipedia. Comment indicates our comments.
CharGP
PerplexityAT
and Mallen et al. (2023) who report QA accuracy of models with retrieval robust to the rarity of entities, FACTSCORE of PerplexityAl still signif- icantly drops as entities are rarer: a relative drop of 50% and 64% observed at the atomic-level and sentence-level, respectively.
75-
50-
50
25.
0
Vary freg
Frey
Melium
Rare
Vary tare
Very frel
Frey
Melium
Raro
Vary rare
Error rates are higher for facts mentioned later in the generation. Figure 2 (bottom) reports fac- tual precision over relative positions in generation. Across all LMs, the later part of the generation has significantly worse precision. This is likely be- cause (a) information mentioned earlier is more frequently mentioned in the pretraining data (c.g.. nationality, profession), and (b) error propagation affects the later part of the generation. This also implies that evaluating LMs solely based on short answers may not provide an adequate assessment of their factual precision, as it fails to account for errors that arise in the later stages of generation.
75-
75-
50-
20
20-40
40.40
60-40
60
20-40
40.69
60-
Figure 2: FACTSCORE across varying frequency levels of human entities (top) and relative positions in gener- ation (bottom). FACTSCORES are lower as the rarity of the entities increases and the position of the fact is later.
Error rates are higher for rarer entities. Fig- ure (top) shows factual precision over varying frequency levels of topic entities (humans) in the pretraining corpora (see Appendix A. There is a notable decrease in FACTSCORE as the rarity of entities increases, consistently across all LM This is in agreement with Kandpal et al. (2022) and Mallen et al. (2023) which show that short question answering (QA) accuracy is highly corre- lated with the entity frequencies in the pretraining data. However, in contrast to Kandpal et al. (2022)
Qualitative analysis of Not-supported. One of the surprising findings in our empricial analysis is that FACTSCORE of PerplexityAl (71.5%) is lower than expected despite having access to the search engine. To better understand its errors, categorize 30 random samples whose label is Not cot-suppprted (Table 2).
Single-sentence contradiction: A single sen- tence from Wikipedia provides direct contradic-

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tion the generation, either word level (numbers, dates, or entities) or beyond.
tion 4.2). FACTSCORE estimated by our model is then used evaluate twelve LMs (Section 4.3).
Page-level contradiction: Errors found after reading the entire page, often because a fact that should have been mentioned in Wikipedia if true is missing, c.g., whether the subject appears in a particular film.
4.1 Model
Our estimator of FACTSCORE first breaks a gen- cration into a series of atomic facts and then vali- dates each against the given knowledge source. We find taking atomic facts generated by InstructGPT (used in data collection in Section 3.3) effective and close to human, consistent with findings from prior work (Chen et al., 2022). This section thus focuses on how to validate each atomic fact against a given knowledge source.
Generation is subjective, often be- cause Perplexity AI copies subjective text from Wikipedia, c.g., directly copying a quote from a journalist without realizing it.
Subjective:
Fact is irrelevant: Generation is irrelevant to the subject due to a search error.
The validation is based on zero-shot prompting of an LM referred to as an LM to distinguish from an LM Specifically, prompt-whose construction methods differ across four variants is fed into an L. The prediction then made by comparing the conditional probability of True and False from the LM If the logit values are unavailable (e.g., commercial LMs like Chat- GPT), the prediction is made based on whether the generated text contains True or False.
Wiki is inconsistent & wrong: In the example, Wikipedia indicates that the subject won one award from the film Kick, but also includes text that they multiple awards from Kick, which is inaccurate and cited news article that does not support the claim.
Annotation error: Annotators assign incorrect labels, typically because the information is not mentioned in the subject's Wikipedia page (likely because it is insignificant).
The four variants we consider are as follows.
No-context LM uses <atomic-fact> True False? as prompt, closely resembling Kadavath et al. (2022).?
We also find that, although PerplexityAl provides citations to the references, citations have little cor- relation with factual precision. 36.0% and 37.6% of supported and unsupported sentences have ci- tations, respectively. Together with independent findings from Liu et al. (2023a), this indicates that commercial LMs that incorporate search and pro- vide citations may not be reliable as expected. More analysis is provided in Appendix A.5.
Retrieve -LM retrieves passages from the given knowledge source and then prompts the LM It first retrieves passages, constructs the prompt by concatenating retrieved passages, the given atomic fact, and "True or False?", and feeds it to the LMEvAL to get the prediction.
Nonparametric Probability (NP) makes judg- ment based on nonparametric likelihood. It masks out each token the atomic fact, computes its like- lihood using a nonparametric masked LM (Min et al., 2023), averages probabilities over all tokens, and makes a prediction based on thresholding. Retrieve Retrieve and NP which assigns Supported only if both methods assign Supported.
4 Estimating FACTSCORE for Automatic Evaluation
Human evaluation of factual precision is costly (S4 per generation) (Bohnet et al., 2022; Krishna et al., 2023) because validating every atomic fact against a large knowledge source is time-consuming, and one generation contains many (26-11) atomic facts. This prevents LM developers and practitioners from evaluating the factual precision in long-form generation of new LM5UBI at scale. In this context, we introduce model that estimates FACTSCORE. This estimator takes set of generations and au- tomatically computes FACTSCORF, and can be applied to any LM
NP is an ensemble of
'In Appendix B.3, compare with an altemative prompt- ing that generates question and compares the answer it and the expected answer (Kryscinski et al., 2020; Wang et al., 2020; Gao al., 2022; Manakul al., 2023). We empirically find that our prompting performs better due the lack of control over the questions being generated.
'n Appendix B.3, we also compare with Self-check LM, a concurrent work from Manakul et al. (2023). We do not include it in the main paper because it has strong restrictions, e.g., requires access to the I LMamn at evaluation time and can- not be applied to Perplexity Al with nondeterministic outputs.
We deseribe our model (Section 4. 1) and demon- strate its accuracy against human evaluation (Sec-

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SUBI: InsIGPT
SUBI: ChatGPT
SUBE: PPLAI
Evaluator
relrv
ranking
ER
FS
ER
FS
ER
FS
Human
42.5
58.3
71.5
Always Supported
$7.5
100.0 +
41.7
100.0 +
28.5
100.0+
Trivial
Always -ot-suppor ted
42.5
0.0
58.3
0.0
71.5
0.0
Always Random
7.5
50.0
8.3
50.0
21.5
50.0
CharGPT I-LLAMA
No-context LM
7.1
49.6
7.8
50.5
34.7
36.8
NP
57.3+
13.7
72.0 +
72.9
Retrieve
56.6
17.1
75.4 +
71.6
Retricve-+I. NP
1.4
41.1
0.4
58.7
9.9
61.6
No-context LM
39.6
82.1
31.7
90.1 +
3.3
74.8
Rctrieve-+1M
5.1
47.6
+
6.8
65.1 +
72.3
Retrieve-+LM NP
5.2
37.3
53.6
62.8
Table 3: Results on Error Rate (ER) along with FACTSCORES estimated by each model (FS). 'retry' indicates whether not retrieval is being used, and 'ranking' / indicates whether the ranking between three L.M rated the model is consistent to the ground truth ranking. + and respectively indicate the estimation is an overestimation and underestimation by more 5% absolute, Red Bold indicates best (lowest) ER. See Appendix B.2 for the results in other metrics that consider individual judgments instead of aggregated ones.
We use LLAMA 7B trained on Super Natural Instructions (Inst-LLAMA, Touvron et al., 2023; Wang et al., 2022) and ChatGPT as LMEvAL, and Generalizable T5-based Retrievers (GTR, Ni et al. (2022)) for passage retrieval. See Appendix B.1 for implementation details.
underestimation of FACTSCORE.
ChatGPT is not always the best. Our results show that ChatGPT is not necessarily better than Inst-LLAMA. We investigate this further in Ap- pendix B.3. In summary, ChatGPT is better at validating each individual atomic fact. However, most errors from ChatGPT are incorrectly assign- ing Supported unsupported facts, overestimat- ing FACTSCORE. contrast, LLAMA+NP is not biased toward overestimation or underestimation of the factual precision, resulting in an aggregated factual precision to be closer to the ground truth. This is similar to the trade-off between system- level and segment-level correlations in summariza- tion evaluation, which often produce different rank- ings (Bhandari al., 2020; Deutsch al., 2021).
4.2 Evaluation of Estimators
Metrics. We report Error Rate (ER) the dif- ference between the ground truth and the estimated FACTSCORE-as well as whether the estimated FACTSCORES preserve the ranking between three LMSuaIS. Appendix B.2 discusses results with other metrics that consider individual judgments instead of aggregated judgments. We use the data in Section 3.3 evaluation data.
Results are reported in Table 3.
The best estimator depends the LM While using retrieval is consistently better than No-context LM, the best variant of estimator de- pends on a LMsuBr: LLAMA+NP for InstructGPT and ChatGPT, and ChatGPT for PerplexityAL. Nev- ertheless, both evaluators give consistently correct ranking between three LMsuai' 's, and Section 4.3 show scores from two estimators are largely corre- lated across 10+ LM (0.99 Pearson's 's r). We recommend users try both variants of estima- when evaluating a new LM and report their correlation.
Retrieval significantly helps. Models that use re- trieval consistently better than No-context LM which either has significantly high ER does not preserve ranking between three LM This is likely because the LM has not memorized ev- ery factual information about the topic entity, thus benefiting from retrieval providing factual context. Nonetheless, just using Retrieve-LLM may over- estimate FACTSCORF, e.g., by up to 17% with Inst-LLAMA, when LM is InstructGPT or ChatGPT. In this case, ensembling Retrieve-<LM and NP reduces error rate by significant mar- gin. When LM Perplexity AI, single meth- ods (either LL or NP) give a low ER, and ensemble methods have higher ER due to an
4.3 Evaluation of New LMs
Our estimator allows evaluating factual precision of a large set of new LMs at scale with no human

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LM
Base LM
Use other LMs Open Release
LMauas
responding
#facts /
InstroctGPT
?
Nov 2022
GPT-4
88.2
60.8
ChaGPT
Nov 2022
Vicuna 13B
76.6
50.9
GPT-4
?
Mar 2025
Vicuna 7B
91.0
45.6
Alpaca (7B.138.68B)
LLAMA
InstructGPT
2023
Vicuna [38]138]
LLAMA
CharGPT
Mar 2023
Oasst-pythia 12B
100.0
39.7
Dolly 12B
Pythia 12B
N/A
Mar 2023
StableLLM-uned-alphh 7B
66.6
38.0
Oasst-pythia 12B
Pythia 12B
N/A
Mar 2023
MPT Chat 7B
88.8
37.3
StableLM-tunee 7B
StaleLMMbasee
CharGPT, GPT-4
Apr 2023
ChatGPT
84,2
37.0
MPT Chat 7B
MPT 7B
CharGPT
May 2023
InstructGPT
99.8
27.7
Dolly 12B
100.0
24.6
Table 4: A set of twelve LMs evaluated in Section 4.3.
Alpaca
100.0
17.4
All models are tuned for instruction following or chat. Use other LMs indicates whether the model is trained on any data that includes outputs of another model. Open indicates model weights are publicly available.
Alpaca 65B
100.0
17.1
Alpaca 13B
100.0
16.6
Human
88.8
29.0
Table 5: Statistics of 500 model-generated bios in our unlabeled data from 12 LMs well as Hamann-wiiten bios. % responding indicates % of generations that do not abstain from responding. #facts res indicates # of atomic facts per response. LMS are sorted based on # of facts per response. See Figure for their FACTSCORES.
efforts. As a case study, we evaluate ten new LMs that came out within two months at the time of con- ducting experiments (Table 4). These LMs were evaluated on many benchmarks but not in factual precision of long-form generation since such eval- uation is costly. We aim to provide new insights these LMs by estimating FACTSCORE of their long-form generations.
with overlap in entities. We additionally include InstructGPT, ChatGPT, and human-writte biogra- phies obtained through DBPedia. Human wwrtten biographies were unavailable for 1 11% of entities which we consider as abstaining from responding. See Table 5 for their statistics. In total, we evaluate 6,500 generations from 13 subjects, which would have cost $26K if they were evaluated by humans.
4.3.1 Setup
We evaluate 10 recently-released LMs as shown in Table 4. GPT-4 (OpenAI, 2023) is multi- modal LM released by OpenAl available through API. Alpaca (Taori et al., 2023) is based LLAMA (Touvron et al., 2023) fine-tuned the instructions data based on InstructGPT fol- lowing the recipe from Wang al. (2022). Vi- cuna (Chiang et al., 2023) is based on LLAMA fine-tuned on the outputs from ChatGPT avail- able through ShareGPT. Dolly" is Pythia 12B (Bi- derman et al., 2023) fine-tuned on DataBricks Dolly, human written data created by Databricks." 10 Oasst-pythia" is Pythia 12B fine-tined human- written data collected through Open Assistant, 12 Stablee.M-tuuned-alphh
4.3.2 Results
Figure 3 shows the ranking between 13 subjects provided by the two best variants of our estimator whose scores are largely correlated, e.g., having a Pearson's 's of 0.99, This evaluation allows a better understanding of these models, including:
All LMs are substantially less factual than hu- mans. This is in contrast to prior work that claims LMs approach human performance, even for complex tasks (Ding et al., 2022; Nori et al., 2023; Lee et al., 2023) even though the task of writing biographies is fairly easy.
based on StableLM- base-alpha fine-tuned on the data used the Al- paca data, DataBricks Dolly, the ShareGPT data, the GPT4All data (Anand et al., 2023) and An- thropic HH (Bai et al., 2022). MPT Chat is based on MPT 7B fine-tuned on the ShareGPT data, the Alpaca data, Anthropic HH, HC3 (Guo et al., 2023), and Evol-Instruec"
GPT-4 and ChatGPT are comparable in factual precision. However, reported in Table 5, GPT- 4 abstains from responding less (12% vs. 16%) and generates significantly more facts (61 vs. 37 per response).
GPT-4 and ChatGPT are significantly more fac- tual than public models.
We prompt each L. to generate biographies of 500 human entities as done Section 3.3 but
Within the same family of models that differ in sizes, there is a clear correlation between the model size and factual precision, e.g., Alpaca 65B 13B 7B, and Vicuna 13B > 7B.
"sharegpt.com "dolly-v2-12b 'ddatabricks com "open-assistant io maast-sft-1--yyhia--- 14 "sstablelm-bar basi--bb--l-p---0 1s MombbleeM-tuued--ee alaeddaappa-771 15 momsaicml wtwm//lo//mptt/////o/oooooo///oe "evol_ instruct_7kk

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ChatGPT
ChaiGPT
GPT4
Alpaca
Alpaca
InstructGPT
InstrueIGPT
Alpaca
13
Vieuna
Alpaca 138
Alpaca
Vieuna
Vieuna 78
Alpaca
MPT-Chat
Ossst-pythia
Ossst-pythin 128
Dolly
Dolly 12B
M 7B
StableLM
Est. FActScore (%)
Est. FActScore (%)
Figure 3: Ranking between 13 subjects (human and 12 LMs), rated by the two best variants of our estimator: ChatGPT (left) and LLAMA+NP (right), both with retrieval. Scores from two metrics have Pearson's of 0.99. See Table 5 for % of responding and # of atomic facts per response of each L.M. The variance in estimation based on different subsets of prompts is reported in Figure Appendix B.4.
Alpaca and Vicuna achieve performance that is very close to each other within the same size of models, possibly because they share the same base model and similar training data. Nonethe- less, as shown Table 5, Vicuna generates sig- nificantly more atomic facts than Alpaca does (51 vs. 17 per response). Also, Alpaca never abstains from answering while Vicuna does.
the case of ChatGPT. Since human evaluation is time-consuming and costly, proposed a model that estimates FACTSCORE, allowing an auto- matic evaluation of FACTSCORE. We found our estimator based on retrieval over a knowledge source and competitive language models estimates FACTSCORE close to the ground truth, and show- cased its application by evaluating 12 recently- released LMs that could have cost $65K if evalu- ated by humans and providing insights about them.
Within public models, there are large gaps in factual precision even when the model size is similar, e.g., within the 7B models, Alpaca and Vicuna (~ 40%) are more factual than MPT- Chat (30%) and StableLM (17%). Possible fac- tors include the choice of the base LM, the data, and the training recipe (Hoffmmann et al., 2022).
Within four months since its initial release, FACTSCORE has actively been used in subse- quent work, evaluating factual precision of recently- proposed models (Ye et al., 2023; Sun et al., 2023; Malaviya et al., 2023; Dhuliawala et al., 2023). As future work, we suggest: (1) considering other aspects of factuality such as recall (coverage of fac- tual information); (2) further improving the estima- for better approximation of factual precision; and (3) leveraging F'ATSCOREE to correct model generations (briefly explored in Appendix C).
We highlight that this evaluation only considers factual precision, specifically in people biographies. A holistic evaluation of LMs should include other aspects of generations such as fluency, coherence, relevance, consistency and creativity, which is out of scope of this paper.
Limitations
5 Conclusion and Future Work
Scope of FACTSCORE. All of our experiments focus on people biographies and Wikipedia, be- cause many LMs can generate biographies with objective and specific facts (rather than subjective and vague ones) and Wikipedia has high coverage for them. FACTSCORE can be applied to broader domain, e.g., text about recent events whose knowl- edge source can be collection of news articles. or text about scientific findings whose knowledge source can be a collection of scientific literature. We present a proof of concept in Appendix B.5 and
We introduced FACTSCORF, a new evaluation of the factual precision of long-form generation from LMs that breaks generation down into a series of atomic facts and computes fraction of facts supported by given knowledge source. We first performed extensive human evaluation, finding that commercial, state-the-art-art LMs- InstructGPT, ChatGPT, and search engine aug- mented, PerplexityAI make a substantial amount of errors, e.g., having a FACTSCORE of 58% in

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leave further study for future work.
Prompt: Tell me bio of Mary I of England.
Due to the assumptions made in Section 3.1, FACTSCORE is not applicable when the facts are more nuanced, open-ended, and debatable (Chen et al., 2019; Xu et al., 2023) or with knowl- edge source whose text frequently conflicts with each other (Wadden et al., 2022). Moreover, FACTSCORE may not be suitable for the human- written text that is nuanced and includes intentional or implicit deception.
Generation: Mary I was Queen of England and Ireland from July 1553 until her death. Mary was the daughter of King Henry VIII and his first wife, Catherine of Aragon. After her father annulled his marriage to Catherine, Mary's 's status was in doubt, and she was excluded from the line of succession. Her half- brother, Edward VI, succeeded their father in 1547, and Mary had to live with the humiliation of being declared illegitimate.
Table 6: An example whose factual precision is high but recall is low. The generation does not mention how Mary I England got back to the line succession and eventually became queen.
Limitation in our estimator. While our estima- for closely approximates humans and provides con- sistent ranking over a large set of LMs, is not per- fect in individual judgments, and the best variant depends on the degree of how close a generation is to Humann-rittenn text and its linguistic complexity. Future work can investigate how the distribution of model generation affects the performance of the estimator and further improve the estimator.
Asai, Yanai Elazar, UW NLP members, UMass NLP members, FAIR lab members for feedback and discussion on the paper.
This research was supported by NSF IIS- 2046248, NSF I1-2202506., NSF I1S-2044600, ONR N00014-18-1-22 18-1-2670, DARPA under Contract No. FA8650- 23-C-7316, an Allen Distinguished Award, and gifts from A12. The views, opinions and/or find- ings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense the U.S. Government. Sewon Min is supported by a J.P. Morgan fellowship, and Kalpesh Krishna was supported by the Google PhD Fellowship.
ONR MURI N00014-
Beyond facctull precision. FACTSCORE focuses on factual wreision-wwetther each piece of in- formation in generation is factually supported by a reliable source of knowledge-whiee which is only one aspect of the broader factuality problem. For instance, FACTSCORE does not consider factual recall: the coverage of information in a genera- tion. FACTSCORE does not penalize a model that abstains from responding too frequently or gener- ates fewer facts, which can be unfair since there is an inherent trade-off between precision and recall. Moreover, the boundary between precision and re- call is often blurry, e.g., it is possible that, even if every piece of information in a generation is sup- ported, it misses significant piece of information that should have been mentioned order to be con- sidered as correctly responding to the input prompt (example in Table 6). We leave more holistic evaluation of factuality for future work, and rec- ommend reporting FACTSCORE together with the % of abstention and the average number of atomic facts (as we did in Section 4.3).
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A Details in Data Collection
Prompt: Tell of Ylona Garia.
Sentence: IYlona Garcia] has since appeared in various TV shows
A.1 Sampling human entities
such as ASAP (All-Star Sunday Aftemoon Party). Wansapanataym
Presents: Annika PINTAsera and Maalaala Mo Kaya.
We sample 183 human entities be annotated as follows. We first choose entities from Wikidata
Ylona Gareia has appeared various TV shows. Supported
She has appeared in ASAP. Supportted
ASAP stands for All-Star Sunday Afterooon Party, Support ted
whose instance of human and have correspond-
ASAP TV show. Supported ted
She has appcared in Wansapanataym Presents: Annika PINTAscra. Not-supporter ted
ing Wikipedia pages. We then categorize entities
based on two dimensions: frequency and nation- ality, resulting in 20 categories. We then sample
Wansapanataym Presents: Annika PINTAsera a show.
Irrelevant
She bas appeared in Maalaala Mo Kaya. Not-supported
entities uniformly at random over all categories.
Maulaala Mo Kaya is a TV show. Irrelevant
Prompt: Tell
of John Estes.
Frequency. We compute freqValue as a max-
Sentence: William Estes is an American actor known for his role on
CBS police drama Blue Bloods as Jameson JamieReagan.
imum of the entity occurrence in Wikipedia pro-
William Estes is American. Irrelevant
vided by Kandpal al. (2022) and the pageview
William Estes is actor. Irrelevant
count of the Wikipedia page following Mallen al. (2023). We found using one of them could lead to an underestimate of frequency lev- els due to failure in entity linking or mismatch in the Wikipedia page title, and taking a maxi- mum of them provides reasonable solution. We then assign one of five categories: "Very rare' if freqValuee [0, 102), 'Rare' if freqValuee [10², 10a), 'Medium' if freqValuee [10*, 10*), 'Frequent' if freqValues [10 10%), and 'Very frequent' if freqValuee [109,).
William Estes is known for his role on CBS police drama Blue
Bloods. Irrelevant
William Estes' role on Blue Bloods Jameson "Jamie" Reugan. Irrolevant
Table 7: Examples that contain Suppor ted, Not not-supported and Irrelevant. Sentences in bullet points indicate atomic facts.
for one prompt, because we find it saves annotation time in total. 10% of the HITs have two workers assigned to calculate the agreement rate; the rest have one worker assigned. The agreement rates are 96%, 90% and 88% for InstructGPT, ChatGPT and Perplexity respectively. Appendix A.5 discusses disagreement cases more detail. The full instruc- tions and the interface are provided in Figure and Figure 7, respectively.
Nationality. We take country of citizenship from Wikidata and assign them one of four cat- egories: 'North America', 'Europe & Middle East', 'Asia & Pacific' and 'Latin/South America & Africa'.
A.2 Details in generating atomic facts
A.4 Examples in annotated data
We break out generation automatically by split- ting a generation into sentences, and feeding each sentence to InstrucIGPT (text-davinci-005 with a series of instructions to further break it down to a series of atomic facts. The prompt to Instruct- GPT is provided in Table 15. Outputs from In- structGPT are used (1) to human experts for revi- sion (Section 3.3) and (2) for model- evalua- tors (Section 4). We find human experts split and merged atomic facts from InstructGPT for 18% and 34% of the cases, respectively.
Table 7 provides examples of the numan-annotatee data, each atomic fact with an assigned label. Supported and Not-supported respectively in- dicate Wikipedia supports the fact and does not support the fact (either contradicts or does not contain any evidence). Irrelevant indicates the fact is irrelevant the input prompt, which can further be divided into two cases: (1) the fact depends on other facts because expands previ- ous facts in generation, and such other facts are Not-supported, e.g., in the first example in Ta- ble 7, and (2) the entire sentence is irrelevant to the prompt, independent from other facts in a gen- cration, e.g., the second example in Table 7. The second case rarely happens with InstructGPT and ChatGPT, but happens considerably with Perplex- ityAl, i.e., 24.7% of generations of PerplexityAl have > sentences marked as irrelevant without de- pendencies to other facts, compared to 0.5% and
A.3 More details on annotator recruitment
We recruit freelancers through Upwork and pay 15-25 USD per hour. We recruit fact-checking experts- freellncess who mentioned fact-checking their expertise-for Step 3. Every worker went through qualification test of 2 hours and was tested to be highly qualified. We design one HIT to consist of three generations, one from each LMSURI,

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Category
Example
Different interpretations of 21 the factual information
Gen Gerhard Fischer is an inventor. Wiki Gerhard Fischer (inventor). was first patented by Dr. Gerhard Fischer in 1931. A metal detector had been invented some forty years carlier (1881) by Alexander Gruham Bell
Gen Chadwick Boseman was a producer. Comment Chadwick Boseman is not known producer, but produced one music video.
Inferred (not directly men- 16 tioned but highly likely)
Gen Leach has since become member of the England Test team. Comment Leach is member of the England Test team, but since when is less clear.
Depends how strict in judg- ing the correctness
Gen le made his Test debut for England March 2018. Wiki On 16 March 2018, he was called up England's Test squad (...) Hle made his debut in the second Test in Christchurch.
Gen The building was the first LEED-cctiffiatted building Edmonton. Miki (.) became the first
project the City Edmonton achieve LEED Gold status.
Suhjective
21 Gen Chadwick Boseman hecame an African American pioneer. Wiki Culture writer Steve Rose, in The Guardian, said Bosernnn' career was revolationary and be "leaves hehind a gamechanging legacy" " (..) Rose wrote: "Chadwick Boseman began his career playing African American icons and pioneers; he ends it as one himselff"
Wikipedia not consistent
5 Sen [Tim Fischer] Ambussador to the Holy See from 2009 to 2012. (..) Australian Ambassaor to the Holy See 2008-2012 Comment The plain text and the table of the Tim Fischer page as well us the Australian Ambassaddr to the Holy See page are inconsistent in his start year.
Ambussador the Holy See from 2009 2012. Wiki ... was later
Two diffcrent entitics
5
Connent Carles J. Alfonso vs. Carlos Alfonso
Mistakes in annotation
Gen Jack Leach left-handed batsman. Comment mentioned the Englond cricket team page, Table Current Squad.
Table 8: Categorization of disagreement cases. Gen indicates the generation from PerplexityAl, and Wiki indicates evidence text from Wikipedia. Comment indicates our comments.
1.3% in InstructGPT and ChatGPT, respectively. This is because Perplexity often directly copies search results even if they are largely irrelevant to the input prompt. This is in agreement with con- current work from Liu et al. (2023a) that shows generative search engines like Perplexity copy incorrect search results and generate text that is irrelevant to the input query.
Chadwick Boseman was producer: Chad- wick Boseman is widely known as another pro- fession (singer) and there is no text that men- tions him as producer. However, he produced one music video.
Nonetheless, since our agreement rate is fairly high (91%), we think such cases are rare in our particular domain of people biographies. We include more discussion on other domains that such cases may be more frequent in the Limitation section.
A.5 Qualitative Analysis
Analysis of disagreement cases. We analyze the cases where two annotators assigned to same generation disagree on a precision label for the same atomic fact. Categorization is provided in Table 8. The 70% is due to an inherent debatability on whether or not the fact is supported by given source of knowledge, not satisfying Assumption 2 in Section 3.1. This is because there can be multiple interpretations of fact, it is debatable whether or not an information can be inferred from a piece of text, or the atomic fact is subjective. For instance:
Coverage of English Wikipedia. While factual prediction is inherently a function of a knowledge source given as part of the input, a potential con- cern is how representative using English Wikipedia as knowledge source for evaluating people biogra- phies with respect to its coverage. For instance, it is possible that, especially for rare entities, the cover- age of information Wikipedia is not high enough, and LMs may be penalized by generating informa- tion that is true even if not supported by Wikipedia (i.e., supported by other sources on the web).
Gerhard Fischer is an inventor: Ger- hard Fischer is widely known inventor of metal detector, and even the title of the Wikipedia article is "Gerhard Fischer (inven- tor)". However, it later turns out that he did not invent a metal detector, rather, he commercial- ized it.
To quantify the effect, we randomly sample 30 unsupported facts from ChatGPT people whose categories are either 'rare" or 'very rare'. and then validate them against the entire web. We found 10% (3 out of 30 facts) are fact supported, even though they are not supported in Wikipedia. An example is [Hibo] Wardere published her memoir ritled "Cur:

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One Woman's 's Fight Against FGM in Britain Today" which is not mentioned in Wikipedia but found from Google Books,
Supported
Not--Suporrtdd
Ground truth 75%
Nonetheless, found that Wikipedia has high coverage and mentions most of the important in- formation that were able to find from any other sources on the weh. This is in agreement with prior work that treated Wikipedia as general knowledge source under the same reason (Chen et al., 2017; Petroni et al., 2021).
Evaluator A Estimated = 85%
FlMoRo= 75%
ER 10%
Evaluator B
Estimated 80%
FIMoRo 67%
ER 5%
Figure 4: case which FIxICro and Error Rate (ER) rank two evaluators differently. Evaluator A is better in Fl FI and Evaluator B is better in ER.
B Details in Estimators
B.1 Implementation details
As an LMEVAL. best commercial LM at the time of conducting ex- periments: LLAMA 65B (Touvron et al., 2023) and LLAMA trained on Super Natural Instructions (Inst-LLAMA, Wang et al., 2022) as the former, and ChatGPT (OpenAI, 2022) as the latter. For computing nonparametrie probabilities, we use single-mask variant of NPM with BM25 as in the original paper (Min et al., 2023), and use 0.3 as thresholding hyperparameter.
the best open LM and the
B.2 Segment-level vs. system-level evaluation
Besides how close the estimated FACTSCORE is to the ground truth FACTSCORE (Error Rate, reported Section 4), we also report MICROO Fl MIcrOO evaluates how well the model validates each individual atomic fact, assuming oracle atomic facts (atomic facts by human experts) are given, and evaluates how good the estimator is in identifying facts that are not Supported (NS), For- mally, let 9 and P be sets of atomic facts in set of generations that have Not-supported as a ground truth label and as predicted label, respectively. We define Fl MI as follows.
For passage retrieval, we use Generalizable T5- based Retrievers (GTR, a large variant), an unsu- pervised dense passage retrieval system (Ni et al., 2022). We restrict retrieved passages to be from the topic entity's 's page, and use k 5. We find estimator is not sensitive to the choice of retrieval system (ablations provided Appendix B.3). As a retrieval corpus, we use the English Wikipedia from 04/01/2023 which is around the time the data annotation was completed, and split each page into passages with up to 256 tokens.
Png P
Png G
2 R.
FIMICRO p R
We call them MICRO because they consider individ- ual decisions rather than aggregated estimation.
ER vs. F1 FIMCRO Fl MICrO cares about the indi- vidual decision, while ER cares about the aggre- gated estimation. An evaluator that has a high (bet- ter) Fl MIcRO but always overestimates or underesti- mates factual precision may have higher (worse) ER, e.g., Evaluator A in Figure Conversely, an evaluator that has lower (worse) Fl mICro but is not biased toward overestimation nor underestima- tion may have a lower (better) ER, e.g., Evaluator B in Figure 4. Prior work in model-based evalua- tion mainly reports aggregated scores since the goal is comparison between different systems being evaluated (Zhang et al., 2020; Rashkin et al., 2021; Gao al., 2022) while we report both to see the relationship between two types of metrics. Fl and ERR are also closely related to segment-level and system-level correlations to human judgments respectively, which have been extensively used
Additional baselines. We also compare with Self-check LM, a method from concurrent work by Manakul et al. (2023). Self-check LM needs multiple samples generated from the LM It val- idates the given atomic fact by prompting LMEvAL conditioning on each generated sample," making judgment (Supported ted or not) from each, and ag- gregates the results through a majority vote. This method assumes (1) the LMsurI available at the time of evaluation and (2) the outputs from the LM are nondeterministic, which makes it not applicable to PerplexityAI.
17 Manakul et al. (2023) uses BERTScore and supervised question answering system instcad of L.M prompting, however, we find LM prompting to be significantly hetter.

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LM
LM
Evaluator
Evaluator
relrv
InstGPT ChatGPT
PPLAI
InstGPT ChatGPT
PPLAI
Always Supported
0.0
0.0
0.0
LLAMA 65B
Always Sut-ouppo
71.4
58.3
30.9
No-context LM
X
22.2
20.0
18.6
Random
52.2
45.0
25.7
Retrieve +LM
54.6
42.1
36.1
Retrieve- NP
80.1
67.1
55.1
No-context LM
61.2
52.2
31.4
Self-check LM
x
66.0
48.4
Inst-LLAMA 7B
No-context LM
X
61.2
52.2
31.4
Retrieve-H -LM
78.7
61.9
51.1
Retrieve LM
78.7
61.9
51.1
NP Retrieve- NP
70.0
56.6
51.4
Retrieve-+1.M NP
83.2
70.5
53.3
83.2
70.5
53.3
ChatGPT
No-context LM Retrieve-
40.0
25.4
25.4
Table 9: Results in FASHCRO using Inst-LLAMA 7B as an M.V "retrv' indicates whether or not retrieval is used. Self-check not applicable PerplexityAl whose outputs are semi-deterministic. Bold indicates the best performance.
87.5
80,2
65.8
Retrieve->1.M NP
86.6
77.8
60.8
Table 10: Ablation in Fl MIcro the choices of LMEvAL. 'retrv' indicates whether or not retrieval used. Bold and Red bold indicate the best F1 within open-access LMs and commercial LMs, respectively.
developing evaluation metrics in machine transla- tion (Ma et al., 2019; Thompson and Post, 2020) and summarization (Bhandari et al., 2020; Deutsch et al., 2021).
different choices of an LM Within the same method, Inst-LLAMA 7B outperforms LLAMA 65B, and ChatGPT outperforms both. Using trieval is critical across all models, e.g., the best no-context model based on ChatGPT underper- formed by all models with retrieval. Using NP helps LLAMA-based models but not ChatGPT, likely because ChatGPT less affected by incor- rect prior from the LM or distracting passages.
Results. Results on FIMICRO are reported in Ta- ble 9. Self-check LM outperforms no-context LM by 4-11 %, which confirms findings from Manakul al. (2023). However, both significantly underper- form methods that use retrieval. This in contrast to Manakul et al. (2023) that reports that Self-check without retrieval achieves performance that is close to that with retrieval, likely because the data in Manakul et al. (2023) contains more frequent en- tities. The fact that retrieval significantly helps is consistent with findings in Section 4.2 with ER metric.
It is worth noting that these results are somewhat different from findings in Section 4.2 that ChatGPT not necessarily better than LLAMA+NP, This is becauase, although ChatGPT better in validat- ing each individual atomic fact, most errors from ChatGPT are incorrectly assigning Supported to Not-supporter facts, resulting in an overestima- tion of FACTSCORE. In contrast, LLAMA+NP is not biased toward overestimation underes- timation of the factual precision, resulting in an aggregated factual precision to closer to the ground truth. This similar to the trade-off be- tween sestem-level and segment-level correlations in summarization evaluation (Bhandari et al., 2020; Deutsch et al., 2021).
Adding NP improves Retrieve >LM by 2-9%, again consistent with findings in Section 4.2. This is likely because Retrieve often makes incor- rect predictions when there is strong bias from an LM there distracting passages, and consider- ing nonparametric probabilities makes the model more robust these factors. For instance, given an unsupported fact Samuel Oboh is Nigerian, No- context LM, Self-check LM and Retrieve->LM pre- dict Sudported due to a strong name-nationality bias. NPM correctly predicts Not-supported based on passage Samuel Oboh Canadian architect, manager, worth noting that this is different from findings in Section 4.2 that ChatGPT is not necessarily better than LLAMA+NP based on ER.
B.3 Ablations
is It is also
QA Prompting vs. TF Prompting As described in Section 4.1, the prompt, so-called TF Prompting. An alterna- tive QA Prompting, which generates question and the expected answer, obtains the answer for the generated question independent from the ex- pected answer, and compares the expected answer
True False part of
Using a stronger LMEvAL significantly improves MICROO Table 10 reports comparison across

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LMSun ChatGPT
Category
Evaluator
InstGPT
PPLAI
No direct evidence from retrieved passages Distracted by other passages
70
17
Always Supported
30.8
37.1
45.0
Atomic fact context-dependent
7
Always Not sup-suppo
35.7
29.1
15.5
Wrong prediction even with the right passage
3
Random
50.5
50.2
43.2
Annotation
3
QA Prompting
No-context LM
56.5
48.8
32.5
Table 13: Categorization 30 samples incorrectly pre- dicted by Re based ChatGPT.
Self-check LM
65.3
63.2
Retrieve-ble -LM
65.3
58.2
47.3
Prompting
No-context LM
57.3
55.3
41.7
cate that all retrieval systems are equally good and Retrieve >LM is not sensitive to the choice of the retrieval system.
Self-check 1.M
68.0
61.9
Retrieve-<LM
78.9
71.4
69.2
Table 11: Results on FlMiCRO- comparing between the QA prompting and TF Prompting. use Inst-LLLMM an LM Self-check is not applicable Perplex- At since Perplexity outputs are semi comi-dttrrminsticc Bold indicates the best Fl MICRO-
Qualitative analysis. Table 13 categories errors made by Retrieve-+LM based on ChatGPT, the evaluator with the best Fl 70% of the errors are due to retrieved passages not providing direct evidence (either support contradiction). These difficult even for state-of-the-art retrieval sys- tems and language models because validating facts often requires reading the entire page rather than a single passage, e.g., an actor not appearing in a particular film. 17% of errors are made because ChatGPT is being distracted by other passages, al- though it assigns a correct label if only particular, correct passage is given.
LM
Retrieval
InstGPT CharGPT
PPL.AI
BM25
78.5
70.8
69.1
GTR Large
78.9
71.4
69.2
GTR xLarge
79.2
71.3
69.0
Table 12: Results on MIMCCO comparing different re- trieval systems: BM25, GTR Large and GTR xI aage, with Retrieve-+LM based on Inst-LLAMA 7B. Bold indicates the best FI
B.4 More details in evaluation of new LMs (Section 4.3)
and the predicted answer. This approach has been widely studied in the summarization literature and recent work in factual precision (Kryscinski et al.. 2020; Wang et al., 2020; Gao et al., 2022; Manakul al., 2023). Table 11 provides comparison be- tween two types of prompting. The TF approach significantly outperforms the QA approach, consis- tently over all methods. Our further analysis finds that this is due to generated questions often being overly vague or ambiguous. For instance, given supported fact Samuel Oboh is an architect. the LM generates What is Samuel Oboh's ' job? question and Architecs swer, and the obtained answer is Vice President, Although both Architect and Vice President are correct, they are not the same, thus the model incorrectly predicts Not-supported. Such cases make the model overpredict Not Not-suppor,ed, lead- ing to many incorrect predictions.
Variance in estimation. Figure 5 reports FACTSCORES estimated by two variants of our estimator in Figure 3 but with 100 random sub- sets of the data. Specifically, chose N samples (out of 500) uniformly random across 20 cat- egories (defined in Appendix A.1) M times and report the average and the standard deviation. We use - {40, 100, 200} and M = 100. Results indicate that the variance is overall low, preserving ranking between 13 subjects in most cases. As ex- pected, the variance is lower as the sample size gets larger. Finally, the estimator based on ER based on LLAMA+NP (bottom) has overall lower vari- ance than the estimator based on ChatGPT (top).
expected
B.5 Feasibility in applying FACTSCORE to other domains
As mentioned in the Limitation section, our pa- per mainly evaluates people biographies us- ing Wikipedia. Evaluating the generalizability of FACTSCORE other types of prompts and other domains is an avenue for future work.
Impact of the choice of retrieval. Table 12 com- pares Retrieve +LM methods based on a few pas- sage retrieval systems, including BM25 (Lin et al., 2021), I), GTR Large and GTR xaarge Results indi-
As proof of conept, we conduct small-scale

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40 samples (2 per categoor))
100 samples (5 per calegory)
200 samples (10 per category)
Human
Human
Human
ChxGGPT
ChalPPT
ChaiGPT
Alpaca 65B
Alpaca 65B
Alpaca 65B
GPT
InstrucGPTT
Alpoca 138
Alpaca 138
Alpaca 138
Vicuna 13B
Vicuna 13B
Vieuna 130
Alpaca
Alpaca
Alpuca 78-
Vicuna
Vicuna
MPT-Chat
MPF-Chat 78
MPT:Chal
pystt-pythia
Ousst-pythia
pystt-yythia
Dolly 128
Dally 128
Delly 128
StableLM 78
Sublee.M 78
Stublel M
100
Est. FActScore (%)
Est. FActScore 1%)
Est. FActSoore (%%
40 samples (2 per category)
100 samples (3 per category)
200 samples (10 per category)
Human
Human
Human
ChaGPP
ChaGPT
ChiGPPT
GPT4
Alpaca 65B
Alpaca 650
Alpaca 650
InsT
InstrucGGPT
InseactPP
Vicuna 13B
Vicunn 13B
Vicuna 13B
Alpaca 13B
Alpaca 13B
Alpaca 130
Vicuna
Vicuna
Vicuna
Alpoca
Alpaca
Alpaca
MPT-Chat 7B
MPT-Chae 7B
MPT Chat 7B
Oasst pythis :28
Ousst pythia 129
Oasst Ousst-gythia 120
Doily
Delly
Dally
Stablel M 78
Stable M 78
Stablel M
25
50
75
25
50
75
25
50
FActScore (%)
FActScore (%5
FActSoore (56
Figure 5: Impact different subsets of random samples prompts. The FACTŠCORES 13 subjects (human and 12 LMs) are rated the two best variants of our estimator: ChatGPT (Top) and LLAMA+NP (Bottom), both with retrieval. The variance overall low, and is lower as the sample size gets larger and with LLAMA+NP (bottom) than with ChatGPT (top).
studies in the NLP domain. We first manually write 10 prompts asking about NLP papers: Tell me summary of spaper-title> and then obtain re- sponses from ChatGPT. Next, rum FACtSCORE against an ACL anthology knowledge source. Finally, we compute an error rate (ER)-a differ- ence between humans' validation (labeled by au- thors) and the model's validation-as we in Sec- tion 4. The ER 7.41 (FAcTScORF from humans being 66.20, and FACTSCORE from the model be- ing 73.61), which is comparable to ER values in people bios shown in Table 3.
preliminary exploration of methods to edit long- form LM generations to reflect factually correct information. We assume we have access to the set of FACTSCORE labels, and measure how good models are at editing incorrect sentences. In other words, we evaluate our editor models independent of the errors arising from the estimator.
C.1 Methods
We adopt a similar set of methods as Section 4.1 for our editing models. All methods below use four exemplar examples for in-context learning which were sampled from our dataset and removed for subsequent analysis. For all methods, we use Ope- nAl's ChatGPT (OpenAI, 2022) the base lan- guage model due its generative capabilities.
This suggests that FACTSCOOEE can generalize beyond people biographies. However, since this is a very small-scale experiment, we strongly encour- age future research to explore the generalizability of FACTSCORE more domains scale.
No-context LM. We feed language models the prompt Input: <sentence> Edit: and ask to edit the text, without any retrieved context.
Editing Experiments
Our experiments in Section 4 focuses automat- ically identifying factual precision errors long- form generations by language models. Can these labels be used to actually correct errors in the long- form generations? In this section, we perform a
Retrv+ To assist editor model, we use passage retrieval system to find supporting evidence from an external knowledge source (Wikipedia in our case). Our retrieval pipeline is

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identical Appendix B.I, but uses retrieved pas- sages instead of 5 due context length restrictions. Atomic Facts. Additionally, we explore whether adding atomic facts and their labels assist a model with fine-grained editing. Specifically, after the input sentence we add information to the prompt of the form Fact 1 (True/False): <atomic fact 1> Fact 2 (True/False): <atomic fact 2>
where || is the set cardinality and HM de- notes a harmonic mean. For this metric, we discard data points where the gold edit did not add new tokens. Similar to ErrLoc, we also remove stop- words, remove punctuation and lowercase strings before calculating EditCorr scores.
Finally, due to the large output space of possible edits, we also adopt metric which rewards paraphrases of the gold edits. We use semantic similarity embeddings from Wiet- ing et al. (2022) which map paraphrases to a simi- lar part of a vector space. We check the similarity between the model edit E' and the gold edit G, nor- malizing it by the similarity between G and the original input X. Specifically,
(3) SIM alignment (SimAl):
This data is also provided in the exemplars.
Non-edit baselines. Finally, we add some triv- ial baselines to lower-bound our editing metrics. Specifically, we measure the performance of input copying (no edits), as well as an editor with ran- dom token dropping / replacement on random 25% subset of tokens.
C.2 Evaluation
Sim max (,,(G,E) s(G, X) 1 sG,(G,X)
In our data collection process (Section 3.3), along with our verification data we also collected gold- standard human written edits. Let X 21, Nxx be the input sentence and gold edited sentence. We evaluate the quality of the model-generated edit (E = = ci...., egg) using three automatic metrics,
where s(A, B) is the semantic similarity score (normalized to [0, 1J) from the model in Wieting et al. (2022). Intuitively, this metric measures how much closer G and E are compared to G and X.
..No be the
C.3 Results
(I) Error Localization (ErtLoc): Our first met- ric measures how well the editor identifies errors within the input sentence. Specifically, we first cre- ate a "token preservation string", marking token 2y in the input sentence X as "Preserved" or "Not Preserved". We then compute the macro-averaged Fl score between the token preservation strings derived from the gold edit and the model gedee--eee edit. We remove stopwords, punctuation and low- ercase all words before performing this calculation. To equally weigh every sentence, Fl scores in- dependently computed for each sentence before a final averaging.
We present our editing results in Table 14. Overall, find that:
All editing models perform better than trivial lower bounds. Overall, we find that all editor mod- els outperform lower-bound baselines like random noise. This even happens in the no-context LM set- ting, where ChatGPT is editing its own output (or search engine augmented Perplexity Al's outputs), but can still perform non-trivial corrections (6.8 ErrCorr for ChatGPT correcting its own outputs vs 0.1 for a random noise editor baseline).
Retrieval significantly helps with editing per- formance. Across all base language models and metrics, augmenting the editor with retrieved para- graphs boosts performance (6.8 16.8 ErrCorr, 4.0 + 9.5 SimAl for ChatGPT correcting its own outputs). We hypothesize that the internal para- metric knowledge in ChatGPT has insufficient in- formation about the topic (as we also observed Section 3.4) to perform fine-grained editing, and using external knowledge from Wikipedia greatly simplifies error localization and correction. This also corroborates with our findings in Section 4.2.
(2) Edit Correctness (EditCorr): Our second met- ric assesses the quality of the additional tokens added by the godel-generated edit. Specifically, we check the token-level Fl score (Rajpurkar et al., 2016) comparing the new tokens added by the gold edit G and the new tokens added by the model- generated edit E. More concretely.
N Noommon
Σ BEG
GEEORR
precision Ncommon
Illes E E,e & XIIl
recall N Ncommon/
11 E G.9. & XIIl
We avoid taking the vector differences between the origi- nal / edited text since edit vectors (Guu et al., 2018) were not explicitly modeled in Wieting et al. (2022).
EditCorr (F1) (Mppreciiionn

---

InstrucIGPT
ChatGPT
PerplexityAl
Editor
ErLLc
EnrCrr
SimAl
EnLLc
EnrCorr
SimAl
ErrLoc
EirCorr
SimAl
Input copying 25% random noise
37.1
0.0
0.0
38.8
0.0
0.0
45.6
0.0
0.0
44.1
0.1
0.5
45.5
0.1
0,4
45.2
0.0
0.3
ChatGT
No-context
49.0
8.5
6.2
45.3
6.8
4.0
48.3
6.2
4.1
No-context + atomic facts
58.7
12.7
10.5
53.4
10.0
6.6
56.0
9.6
6.1
Rctry-LM
52.6
21.8
15.7
43.9
16.8
9.5
46.3
13.5
6.8
<etrv->LM- atomic facts
65.4
30.4
25.5
63.5
28.3
19.3
62.4
23.6
15.9
Table 14: Results after automatic editing with ChatGPT assuming ground truth verification labels. All editors perform better than trivial lowerbound baselines, and using retrieval and atomic fact labels boosts editing performance. Details of automatic metries (ErrLoc, ErrCorr, SimAl) defined in Section C.2.
Atomic fact labels improve error localization and improve editing performance. Across all base language models (with or without retrieval) we observe that providing fine-grained atomic fact labels improves editing performance (16.8 -> 28.3 EnrCorr, 9.5 + 19.3 SimAl for ChatGPT correct- ing its own outputs). Fine- emneggraned fact correctness labels help the editor easily identify problematic tokens, as seen by the consistent improvements in ErrLoc scores (43.9 63.5 for ChatGPT correct- ing itself). We hypothesize atomic facts help guide the editor with its editing process (for instance, perform more targeted search in the retrieved paragraphs), resulting in EnrCorr improvements. We also find that atomic fact labels reduces the frequency of editor copying the input verbatim or saying The input has no errors from 37.3% 3.9%.
PerplexityAI outputs are the hardest to edit. Overall, we find the highest editing success for InstructGPT, followed by ChatGPT and the least success for Perplexity Al. We hypothesize this is because Perplexity AI already uses search engine, errors are much more subtle as extensively dis- cussed in Appendix A.5.

---

Please breakdown following sentence independent facts: He made his acting debut the The Moon Sun's Dream (1992), and continued
appear in small and supporing roles throughout the 1990s.
acting debui
filmn.
He made his acting dobut in The Moon the Sun's Dream.
The Moon is the Sun's Dream film.
The Moon is Sun's Dreum was released in 1992.
Afler his ucting debut, he uppeared in small and suppocting roles.
After acting dobut, appeared
and supporting roles throughour 1990s.
breakdown following sentence independent
successful producer ongineer, having worked wide variety
including Willie Nelson, Tim McGram, and Taylor Swift.
Ie successful.
He producer.
He engincer.
wide variery artists.
Willie Nebson is an artist.
with Willic Netson.
Tim McGraw is an artist.
He has worked with Tim McGiraw.
Taylor an
He has worked with Taylor Swift.
Please breakdown the following sentence into independent facis: In 1963, Collins became one of the third group of astronauts selected by NASA and be served hack-uup Command Mochile Pilot for the Gemini 7
Collins became an astronaui.
Collins bocame one of the third group astronauns.
Collins hecame one
group
Collins becume one of the third group of astronauts selected by NASA. group astronauts
Collins became
by NASA
He served as the Command Module Pilot.
He served the back up Command Module Pilot.
He served as the Command Module Pilor for the Gemini 7 mission.
breakdown following sentence into independent facts: addition acting roles,
two
currently in development on his leature debut.
acting
Bateman has writen two shoet films.
Bateman has directed two short films.
Batoman has written and directed two short films.
Bateman is currently in development on his feature dehat.
Please breakdown the following sentence into independent facts: Michael Collins (born Octoher 31, 1930) is retired American astronaut and test pilot who mission
Command Modulc Pilot the
Miccael Callins was born on October 31, 1930.
Michaal Collins retired.
Michhel
American.
Michael Collins was an astronaut.
Collins
pilot.
Michael Callins was the Command Module Pilot.
Michnel Collins the Command Medule Pilot for the Apolle mission.
Michael Collins was the Command Module Pilot for the Apollo mission in 1969.
breakdown following sentence into independent He was un American.
an compaser, conductor, musical
composer.
He was conductor.
He musical director.
Please breakfown the following sentence into independent faces: She currently stars the romantic comedy series, Love and Destiny, which premiered 2019. currently stars
Destiny. Love and Destiny is romantic comedy series.
and Destiny premiered 2019.
Please breakdown the following sentence into independent facts: Daring his professional career, McCly played for the Broncos, the San Diego Charpers, the
Minocsota Vikings, and the Jacksonille laguas.
McCoy played for the Broncos.
McCoy played
during professional
McCog played for the San Diego Chargers.
McCoy played for the San Diego Chargers during his professional career.
McCoy played for the Minnesota Vikings.
McCoy played for the Minnesota Vilkings during his professional career.
McCoy played
lacksowcill Jaguars.
McCoy playedd for the Jacksonville Jaguars during his professional career.
Please breakdoww the fallowing centence into independent facts
Table 15: A prompt given to InstructGPT to generate atomic facts for a given sentence. Model generated atomic facts were revised by human editors.

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Instructions (Click to collapse).
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Figure 6: Instructions for data annotation in Section 4. We also provided demonstration video, and gave feedback 1-1 during the qualification task.

---

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Download drafts
Figure 7: An interface for data annotation in Section 4. Annotators were able navigate Wikipedia the left. They annotate three pieces of generations from three LMs for the same prompt in one HIT since it saves time. Since completing one HIT takes considerable amount of time (25min), we added a function that allows saving their work at any stage in the middle of the HIT.


Reviews:
[{"review_id": "kITYZq5iG6", "reviewer": "Reviewer_4fZt", "summary": "", "questions": "A. In which measure Figure 2 (rare entities has less correct biography) could be explained through the fact that the human annotators did not find those particularly facts on the Wikipedia page? Did you look if there is a correlation between the type of unsupported fact (page level contradiction or annotation level) and the popularity/completeness of the page of entities? Is it possible that for the rare entities the fact is true, just not present on Wikipedia?\n\nB. Did you consider the setting in which the LM receives in the prompt the Wikipedia page of the entity and has the task of creating a biography? The model is very likely to produce more accurate results, which will be consistent with the findings you have when you create the automatic measure.", "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 for your thorough comments and feedback. We are glad you found that the problem tackled in the paper is interesting, the idea of using smaller units is valuable, and the work has rigorous experiments.\n\n> The FactScore metric is a very simple metric that cannot be counted as a contribution.\n\nWe believe introducing a simple and easy-to-use metric is a valuable and important contribution to the community that fosters reproducible and scalable evaluation, as it has been the case in past work like BERTScore [1]. Moreover, we think breaking down into atomic facts and validating each is not a trivial idea and has not been done in prior work, e.g., prior work considers a coarser-level validation, as described in Section 2.\n\n[1] Zhang et al. BERTScore: Evaluating Text Generation with BERT. ICLR 2020.\n\n> It hides a complex discussion of what constitutes a fact and when a fact is truly supported by an underlying KB/document.\n\nWe agree with the reviewer that what constitutes a fact and whether it's truly supported by an underlying document are indeed non-trivial questions. We included such discussion in the Limitation section, and highlight our contribution is to introduce an evaluation scheme that (a) works well in the domain where what constitutes “supported” is relatively straight-forward, and (b) is easily extendable.\n\n> For example, if a sentence is not correctly split in the underlying parts, evaluating its truth value is non trivial, for example: President Trump no longer lives in the White House. Here we have 2 facts (Trump, is, President), (Trump, no longer lives in, White House). \n\nWe agree that decomposition of an input with a highly complex linguistic structure is generally harder. However, in practice, model generations tend to have much simpler structure so such cases are very rare. For instance, in our experiments, only 3 out of 1241 sentences from ChatGPT include a negation.\n\nMoreover, there is no assumption we are making that would prevent the model from not being able to handle negations. In fact, we found that our atomic fact decomposition model can handle them. See [the screenshot](https://ibb.co/Fb35259) on how our model correctly handles three sentences from ChatGPT that include a negation.\n\nWe will include more in-depth discussion on this matter in the Limitation section in the next version of the paper.\n\n> Also negations are an added layer of difficulty as often KBs or documents do not have all possible negations if any.\n\nSince we are using a language model to validate each fact, and the language model can generalize reasonably well, it can make a correct validation even if the knowledge source does not include an explicit negation.\n\n> The human analysis of hallucinations has also the drawback that it does not consider that some facts might be true, but not present in the Wikipedia page. This can be more serious for entities with shorter Wikipedia pages, for which the authors find that models tend to produce less accurate results. \n\nTo quantify how big of an issue this is, we ran new, small analysis experiments as follows. We randomly sampled 30 unsupported facts from ChatGPT on people whose categories are either “rare” or “very rare”, and then validated against the entire web. We found 10% (3 out of 30 facts) are supported even though they are not supported in Wikipedia. An example is “[Hibo] Wardere published her memoir titled <Cut: One Woman's Fight Against FGM in Britain Today>\" which is not mentioned in Wikipedia but is found from Google Books. Since these cases are rare, we believe this does not change the conclusion from the paper.\n\nNonetheless, we found that Wikipedia has a high coverage and mentions most of the important information that we were able to find from any other sources on the web. This is in agreement with prior work that treated Wikipedia as a general knowledge source under the same reason [1, 2, and more].\n\n[1] Chen et al. ACL 2017. Reading Wikipedia to Answer Open-Domain Questions.\n[2] Petroni et al. NAACL 2021. KILT: a Benchmark for Knowledge Intensive Language Tasks.\n\n> A. In which measure Figure 2 (rare entities has less correct biography) could be explained through the fact that the human annotators did not find those particularly facts on the Wikipedia page? Did you look if there is a correlation between the type of unsupported fact (page level contradiction or annotation level) and the popularity/completeness of the page of entities? Is it possible that for the rare entities the fact is true, just not present on Wikipedia?\n\nAs mentioned earlier, based on our analysis on “rare” and “very rare” categories (we conducted this analysis for “rare” and “very rare” because we agree with the intuition that the completeness of Wikipedia will be better in other categories), only 10% were the cases where the fact can be supported by other sources on the web but are not supported in Wikipedia due to its incompleteness. This does not change the conclusion from the paper, e.g., adding 10% to the “Rare” and “Very rare” in Figure 2 does not change the overall trend of the figure.\n\n> Finally, models might produce much more accurate results if given in input the Wikipedia page - an hypothesis which is very interesting to test.\n\n> B. Did you consider the setting in which the LM receives in the prompt the Wikipedia page of the entity and has the task of creating a biography? The model is very likely to produce more accurate results, which will be consistent with the findings you have when you create the automatic measure.\n\nWe evaluated models using the Wikipedia article in the input context in our Perplexity AI setting, which is one of the three models evaluated in Section 3. Perplexity AI is a retrieval augmented system that searches for relevant reference documents from the English Wikipedia, feeds them together with the original prompt to ChatGPT, and generates a response [1]. Indeed, Perplexity AI has a much better FActScore than ChatGPT (71.5 vs 58.3). Overall, we think it is important to evaluate settings both with and without the relevant sources included in context, especially given that no Internet access is the default setting of many LM applications like ChatGPT.\n\n[1] - A podcast with Perplexity AI’s CEO confirming this: https://youtu.be/YSYQZZu4MEM?si=GTK1yZO52348BnOL&t=323", "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": "I found the paper well written and easy to follow. In my opinion, the work is sound and the experimental evaluation rigorous. In addition, the problem studied is interesting. The authors invested a lot of money and effort in the experimental evaluation of hallucinations, which is the main strength.  In particular, they do a human evaluation of biography generation for three models: InstructGPT, ChatGPT, PerplexityAI on 183 generations (biographies) from each model. The humans split the generations in smaller units of content that can be easily verified by checking the Wikipedia page of the person.", "reasons_to_reject": "The FactScore metric is a very simple metric that cannot be counted as a contribution, as it hides a complex discussion of what constitutes a fact and when a fact is truly supported by an underlying KB/document. For example, if a sentences is not correctly split in the underlying parts, evaluating its truth value is non trivial, for example: President Trump no longer lives in the White House. Here we have 2 facts (Trump, is, President), (Trump, no longer lives in, White House). Also negations are an added layer of difficulty as often KBs or documents do not have all possible negations if any. Hence, the true contribution of the paper is more in the human analysis of the hallucinations of current models.\nThe human analysis of hallucinations has also the drawback that it does not considers that some facts might be true, but not present in the Wikipedia page. This can be more serious for entities with shorter Wikipedia pages, for which the authors find that models tend to produce less accurate results.\nFinally, models might produce much more accurate results if given in input the Wikipedia page - an hypothesis which is very interesting to test.", "questions_to_authors": "A. In which measure Figure 2 (rare entities has less correct biography) could be explained through the fact that the human annotators did not find those particularly facts on the Wikipedia page? Did you look if there is a correlation between the type of unsupported fact (page level contradiction or annotation level) and the popularity/completeness of the page of entities? Is it possible that for the rare entities the fact is true, just not present on Wikipedia?\n\nB. Did you consider the setting in which the LM receives in the prompt the Wikipedia page of the entity and has the task of creating a biography? The model is very likely to produce more accurate results, which will be consistent with the findings you have when you create the automatic measure.", "paper_topic_and_main_contributions": "The paper investigates hallucinations in generated biographies. The main contribution is in the in depth analysis of hallucinations in the generated biographies, with the added benefit of considering SOTA language models. FACTScore, the metric proposed by the authors, is simply the average truth score of a sentence composed of several facts, where each fact gets 0 (not true) or 1 (true) score. This score is computed by humans in a large scale evaluation, but the authors also propose a few models that can achieve good results.", "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": {}}, {"review_id": "1ccBpvtcUy", "reviewer": "Reviewer_Bgsm", "summary": "", "questions": "- A: What kind and size of the corpus were the NP models trained?\n\n- B: Is there any reason there are no NP results for ChatGPT in Table 3?\n\n- C: The reviewer thinks it is difficult for LLMs to generate facts without referring to any sources.\nConsidering that, what do the authors think is more valuable to evaluate it in a setting that generates text including facts from given relevant sources? \n  - This setting would also have the advantage of evaluating \"factual recall\" from the facts mentioned in given relevant sources.", "limitations": "", "rating": 4, "confidence": 3, "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 for your detailed comments and feedback. We are glad you found that the results and analysis in the paper are interesting and that the evaluation metric is easy to use and open-source is a valuable contribution.\n\n> The generality needs to be clarified because the experiment was conducted only on Biographies.\n\nFactScore on its own is not limited to people's biographies — our main contribution is a general evaluation scheme which involves atomic fact decomposition, and evaluating the factuality of individual atomic facts. While in our paper we focus on biographies, as a proof of concept we ran some additional experiments in the “NLP domain” to show its generalizability.\n\nOur procedure was the following: (1) we manually wrote 10 prompts asking about NLP papers: “Tell me a summary of <paper_title>”; (2) we obtained responses from ChatGPT; (3) we next ran FActScore against an ACL anthology as a knowledge source, and (4) finally, we computed an error rate (ER)—a difference between humans’ validation (labeled by authors) and the model’s validation—as we did in Section 4. The ER is 7.41 (FactScore from humans: 66.20; FactScore from the model: 73.61), which is comparable to ER values in people bios shown in Table 3. This strongly suggests that FactScore can generalize beyond people's biographies.\n\nOverall, more thorough analysis in non-biography domains is an interesting direction for future work since it's possible that different models will be factual to different extents on different domains.\n\n> The method's validity needs to be clarified because the error compared to the case where the correct facts are considered, which is not written in knowledge sources, has not been evaluated.\n\nTo quantify how big of an issue this is, we ran new, small analysis experiments as follows. We randomly sampled 30 unsupported facts from ChatGPT on people whose categories are either “rare” or “very rare” (based on Reviewer 4fZt’s point that this would be most problematic for rare entities), and then validated against the entire web. We found 10% (3 out of 30 facts) are true even if not presented in Wikipedia. As statistics indicate, these cases are relatively rare and have negligible impact to the FactScores returned by our model.\n\nWe also highlight that we found that Wikipedia has a high coverage and mentions most of the important information that we were able to find from any other sources on the web. This is in agreement with prior work that treated Wikipedia as a general knowledge source under the same reason [1, 2, and more].\n\nPlease also note that if a fact is not mentioned anywhere on the web, there would be no way one can validate it (which is why we use the term “supported” and “unsupported” rather than “true” and “false”), and ChatGPT would not be able to generate it either.\n\n[1] Chen et al. ACL 2017. Reading Wikipedia to Answer Open-Domain Questions.\n[2] Petroni et al. NAACL 2021. KILT: a Benchmark for Knowledge Intensive Language Tasks.\n\n> Since it is difficult for LLMs to generate facts without referring to any sources in principle, evaluating it in a setting that generates text including facts from given relevant sources may be more valuable.\n\nWe evaluated models using the Wikipedia article in context in our Perplexity AI setting, which is one of the three models we evaluated. Perplexity AI is a retrieval augmented system that searches for relevant reference documents from the English Wikipedia, feeds them together with the original prompt to ChatGPT, and generates a response [1]. Indeed, Perplexity AI has a much better FActScore than ChatGPT (71.5 vs 58.3).\n\n[1] - A podcast with Perplexity AI’s CEO confirming this: https://youtu.be/YSYQZZu4MEM?si=GTK1yZO52348BnOL&t=323\n\n> A: What kind and size of the corpus were the NP models trained?\n\nThe NP model is trained on Wikipedia and news, on top of RoBERTa, which is trained on Wikipedia and books.\n\n> B: Is there any reason there are no NP results for ChatGPT in Table 3?\n\nIn fact, the NP-only model does not use any language model, although we list it “I-LLAMA” in Table 3. We will modify the table to prevent the confusion.\n\n> C: The reviewer thinks it is difficult for LLMs to generate facts without referring to any sources. Considering that, what do the authors think is more valuable to evaluate it in a setting that generates text including facts from given relevant sources? This setting would also have the advantage of evaluating \"factual recall\" from the facts mentioned in given relevant sources.\n\nWe agree with the reviewer that it is valuable to evaluate LLMs with relevant sources in context. However, we believe it is equally important to evaluate models like ChatGPT *without* the Wikipedia article included in context. By default, ChatGPT does not assume access to the internet, and several users solely rely on its parametric knowledge for information-seeking needs. Overall, we believe it is critical for a chatbot to output factually correct statements, or acknowledge that it is not familiar with the information (rather than hallucinate).\n\n> Reproducibility: 2\n\nWe include all details to reproduce results in the Appendix, and will open-source the code and the data through the PIP package, as highlighted in the abstract.", "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 new score for evaluating factual precision and a method for automatically estimating the score without manual labor.\n- The paper reports some interesting results by evaluating the scores of various large language models.\n- There are plans to release it as open source.", "reasons_to_reject": "- The generality needs to be clarified because the experiment was conducted only on Biographies.\n- The method's validity needs to be clarified because the error compared to the case where the correct facts are considered, which is not written in knowledge sources, has not been evaluated.\n- Since it is difficult for LLMs to generate facts without referring to any sources in principle, evaluating it in a setting that generates text including facts from given relevant sources may be more valuable.", "questions_to_authors": "- A: What kind and size of the corpus were the NP models trained?\n\n- B: Is there any reason there are no NP results for ChatGPT in Table 3?\n\n- C: The reviewer thinks it is difficult for LLMs to generate facts without referring to any sources.\nConsidering that, what do the authors think is more valuable to evaluate it in a setting that generates text including facts from given relevant sources? \n  - This setting would also have the advantage of evaluating \"factual recall\" from the facts mentioned in given relevant sources.", "paper_topic_and_main_contributions": "This paper proposes an evaluation method of factual precision called FACTSCORE, the percentage of pieces of information as atomic facts supported by a given knowledge source.  Since it is expensive to calculate this evaluation metric manually, this paper also proposes a method for automatic evaluation.\n\nThe evaluations of several commercial language models showed that\n  1. infrequent entities tend to have lower  FACTSCORE,\n  1. FACTSCORE of facts mentioned later in generated text tends to be lower, and\n  1. even PerplexityAI, which uses search results, has a low FACTSCORE.\n\nThe experiment results also showed that the proposed automatic evaluation method can estimate FACTSCORE with a relatively low error rate for human evaluation.\nIn addition, the automatic evaluation of FACTSCORE for 12 large language models showed several interesting tendencies such that\n  1.  none of the models is very high,\n  1.  GPT-4 and ChatGPT are comparable, and\n  1. GPT-4 and ChatGPT are better than other published models.", "excitement": 4, "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": "du7kQQQCpK", "reviewer": "Reviewer_4um9", "summary": "", "questions": "- In Page 3, line 484, \"and ChatGPT for PerplexityAI.\"  In Table 3, LLAMA is the best for PerplexityAI with ER=0.1, so my understanding is that the authors choose ChatGPT (with ER=0.8) because it preserves the ranking while LLAMA does not. Is it correct?\n\n- I wonder if the model size is dominant in factual precision because ChatGPT (GPT-3.5 or InstructGPT) and GPT-4 are known to be much larger than the other models discussed in the paper though no exact information on model sizes is available for them. Could you elaborate more about this if you hit upon anything? I read through the paper including Appendix and could find relevant discussions only in 4.3.2, but may have missed something important about the model size.\n    ", "limitations": "", "rating": 3, "confidence": 3, "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 for your insightful feedback and comments. We are glad you found FActScore  a novel formulation of evaluation and the paper well-written.\n\n> It is not explicitly stated how the findings discussed in Section 4 are utilized for deeper understanding of a model for factuality.\n\nWe included in-depth discussion with respect to human evaluation (in Section 3.4) and a large set of LMs (in Section 4.3). If the reviewer has suggestions on more discussion, we are happy to incorporate it in the next version of the paper.\n\n> Since analysis was done over only People biographies and Wikipedia for prompts and a knowledge source respectively, it is not clear how universal the findings are, which is also pointed out by the authors.\n\nFactScore on its own is not limited to people's biographies — our main contribution is a general evaluation scheme which involves atomic fact decomposition, and evaluating the factuality of individual atomic facts. While in our paper we focus on biographies, as a proof of concept we ran some additional experiments in the “NLP domain” to show its generalizability.\n\nOur procedure was the following: (1) we manually wrote 10 prompts asking about NLP papers: “Tell me a summary of <paper_title>”; (2) we obtained responses from ChatGPT; (3) we next ran FActScore against an ACL anthology as a knowledge source, and (4) finally, we computed an error rate (ER)—a difference between humans’ validation (labeled by authors) and the model’s validation—as we did in Section 4. The ER is 7.41 (FactScore from humans: 66.20; FactScore from the model: 73.61), which is comparable to ER values in people bios shown in Table 3. This strongly suggests that FactScore can generalize beyond people's biographies.\n\nOverall, more thorough analysis in non-biography domains is an interesting direction for future work since it's possible that different models will be factual to different extents on different domains.\n\n> In Page 3, line 484, \"and ChatGPT for PerplexityAI.\" In Table 3, LLAMA is the best for PerplexityAI with ER=0.1, so my understanding is that the authors choose ChatGPT (with ER=0.8) because it preserves the ranking while LLAMA does not. Is it correct?\n\nThis is correct, since ER 0.8 is still considered to be very low. Generally, our recommendation is to use a few different variants of the estimator to make sure they give consistent results, which we verify to be true in Section 4.3.\n\n> I wonder if the model size is dominant in factual precision because ChatGPT (GPT-3.5 or InstructGPT) and GPT-4 are known to be much larger than the other models discussed in the paper though no exact information on model sizes is available for them. Could you elaborate more about this if you hit upon anything? I read through the paper including Appendix and could find relevant discussions only in 4.3.2, but may have missed something important about the model size.\n\nThis is a great point. We do believe it is likely to be the case that some models are more factual than others because they are larger, which is in agreement with prior work that shows that larger LMs memorize factual knowledge better (CITE). We did not make this comment about OpenAI models since their sizes are unknown, and only made a relevant comment for open-sourced models whose sizes are known (Section 4.3.2).\n\n> Typos Grammar Style And Presentation Improvements\n\nThank you for pointing out typos and comments on presentations. They will be incorporated in the next version of the 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": "- FACTSCORE is a novel formulation of evaluation of factuality and addresses the problem of existing sentence-level evaluation methods.\n- Though most of the details are in the Appendix, the paper is very well organized and each section is clearly explained.\n  ", "reasons_to_reject": "- It is not explicitly stated how the findings discussed in Section 4 are utilized for deeper understanding of a model for factuality.\n- Since analysis was done over only People biographies and Wikipedia for prompts and a knowledge source respectively, it is not clear how universal the findings are, which is also pointed out by the authors.\n", "questions_to_authors": "- In Page 3, line 484, \"and ChatGPT for PerplexityAI.\"  In Table 3, LLAMA is the best for PerplexityAI with ER=0.1, so my understanding is that the authors choose ChatGPT (with ER=0.8) because it preserves the ranking while LLAMA does not. Is it correct?\n\n- I wonder if the model size is dominant in factual precision because ChatGPT (GPT-3.5 or InstructGPT) and GPT-4 are known to be much larger than the other models discussed in the paper though no exact information on model sizes is available for them. Could you elaborate more about this if you hit upon anything? I read through the paper including Appendix and could find relevant discussions only in 4.3.2, but may have missed something important about the model size.\n    ", "paper_topic_and_main_contributions": "This paper addresses the problem of evaluating the factuality of long-form text that is generated by large language models.\n\nThe authors propose FACTSCORE, a new scoring mechanism that takes into consideration each of atomic facts in the generated text.\nFACTSCORE enables more fine-grained evaluation and comparison of the models.\n\nSince a straightforward way of annotating data by human annotators is time-consuming and costly, the authors also propose an automatic approach of approximately estimating the value of FACTSCORE by using other models for retrieval and prediction of whether or not each atomic fact is supported by the pre-defined knowledge source.\n\nThorough analysis of the estimation for 12 different LMs indicates some interesting findings on correlation between factual accuracy and the model size, training data and instruction tuning, etc.\n\n  ", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Page 3, line 217: I think that [a is supported by C] can be written by the indicator function https://en.wikipedia.org/wiki/Indicator_function  . Or simply f(y) can be written as |{a \\in A_y | a is supported by C}| / |A_y| .\n- Page 6, line 406: across five variants -> \"four\" variants?"}}]

Author rebuttal:

Meta-review: Pros:
1. Paper is well written and easy to follow. Work is sound and the experimental evaluation rigorous. 
2. Problem is interesting and critical in the area of text-based generative AI -- a new score for evaluating factual precision  of outputs from LLMs automatically.
3. Simple metric. Insights from evaluation of FACTSCORE for 12 large language models are interesting.
4. Contribution to open source is a strong positive.

Cons:
1. Work is very empirical, sometimes leading to lack of concrete definitions, e.g., "facts". But this is rightly pointed out in Limitations section. And EMNLP is an empirical venue.
2. Generalization is a problem since the work deals with 1 domain and 1 knowledge base. 
3. Incompleness of the knowledge base could hurt the evaluation. But the work stands on its own assuming that incompleteness of knowledge base is a separate problem to solve.

Suggestions:
For the last 2 points in cons section: Experiments done as part of rebuttal are not very rigorous and done on small scale. While authors are encouraged to include them as part of appendix in the revised submission, this weakness should be explicitly called out in the Limitations section.
Decision: Accept-Main