Enhancing Health Fact-Checking with LLM-Generated Synthetic Data

Jiahe Qian; Jingze Zhang; Yifan Peng; Yiliang Zhou

REVIEW 4 major objections 4 minor 11 references

Enhancing Health Fact-Checking with LLM-Generated Synthetic Data

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read LLM-generated sentence–fact tables supply synthetic training data that improve health fact-checking F1 by up to 0.049 on SciFact and 0.019 on PubHealth.

desk verdict A neat synthetic-data pipeline that is undermined by a dev-set-as-test evaluation; the SciFact '0.049 gain' is not measured on the benchmark's test split. read the letter →

arxiv 2508.20525 v1 pith:Z2B3R4SH submitted 2025-08-28 cs.AI

Jingze Zhang , Jiahe Qian , Yiliang Zhou , Yifan Peng This is my paper · ORCID

classification cs.AI

keywords healthfact-checkingsyntheticdatagenerationlargelanguagemodelssentence-factentailmenttableBERTfine-tuningPubSciFacthallucinationdetection

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes a pipeline that turns unlabeled source documents into labeled training data for health-related fact-checking. It summarizes each document, breaks the summary into atomic facts, and asks an LLM to build a sentence–fact entailment table recording whether each sentence supports each fact. From that table it samples sentence sets and facts to create synthetic text–claim pairs with binary true/false labels, then fine-tunes a BERT-based classifier on the original data plus the synthetic data. Experiments on PubHealth and SciFact report F1 improvements of up to 0.019 and 0.049 over training on original data alone. A pilot study uses the same table to flag hallucinated facts in LLM-generated summaries.

What carries the argument

The load-bearing object is the sentence–fact table: for each document, rows are sentences, columns are atomic facts extracted from an LLM-generated summary, and each cell records whether the sentence entails the fact. The table converts expensive human annotation into a lookup: any sampled combination of sentences plus a chosen fact is automatically labeled true if at least one selected sentence supports it and false otherwise. It also supplies the interpretable device used in the hallucination-detection pilot, where an unsupported column indicates a likely fabricated fact.

What would settle it

Take a random sample of sentence–fact pairs from the pipeline, have human annotators judge entailment, and compare with GPT-4's table entries; if agreement is low, or if replacing the table labels with random labels reproduces the F1 gains, the pipeline's benefit is not coming from the entailment table.

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Extended reading notes

Core claim

The central claim is that an LLM-generated sentence–fact entailment table is a reliable source of synthetic supervision for fact-checking. Given a grounding document, the pipeline has GPT-4 summarize it, decomposes the summary into atomic facts, labels every sentence–fact pair for entailment, and then samples subsets of sentences together with one fact to form claim–text pairs whose labels are read directly off the table. Fine-tuning SciBERT on original plus synthetic pairs yields F1 gains of up to 0.019 on PubHealth and 0.049 on SciFact compared with training on the original data only, with the best gains occurring at different sentence-selection proportions for different data subsets. The same table, populated by the trained model, can expose facts in a summary that no source sentence supports, providing a hallucination-detection signal.

Load-bearing premise

The synthetic labels are only as good as GPT-4's judgments that a document sentence entails a given atomic fact, and the assumption that its summaries are factually consistent with the source; if either is noisy, the gains shrink or reverse.

Editorial extensions

If this is right

Repeating the four-step pipeline can generate a large volume of labeled pairs from the same source documents, easing the annotated-data bottleneck in health fact-checking.
A small BERT-based model fine-tuned this way can verify multiple facts against a grounding document, not just single claim–document pairs.
The same sentence–fact mechanism can flag likely hallucinations in LLM-generated summaries by marking facts no source sentence supports.
The optimal fraction of sentences to sample varies across datasets and subset sizes, so the sampling proportion is a tunable hyperparameter rather than a fixed rule.
Larger training subsets tend to support higher F1 under the best proportion, consistent with the synthetic augmentation providing increasingly useful supervision.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same pipeline could be applied to other evidence-grounded verification tasks, such as legal or financial claim checking, wherever documents can be decomposed into atomic facts; the paper only evaluates health and scientific claims.
The sentence–fact table itself, not just the augmented classifier, is a reusable artifact: one could use it to inspect which parts of a document support a claim and to generate contrastive negative examples for training.
A testable extension is to compare GPT-4's table labels against human entailment judgments; if agreement is low on a sample, the observed F1 gains should shrink accordingly.
Because the label distribution and difficulty of synthetic pairs are controlled by sampling proportion, the pipeline could be tuned to emphasize hard false claims or rare topics, something the paper did not explore.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a reasonable proof-of-concept for LLM-generated synthetic training data in health fact-checking, but the headline SciFact result is not a test-set result, and the PubHealth numbers are selected over 10 proportion settings with no error bars. The core idea is worth reading; the evaluation needs serious work.

What's new: the sentence-fact entailment table, built by GPT-4 summarization, FActScore-style atomic decomposition, and per-sentence support judgments, is a clean way to produce claim-text pairs with labels from unlabeled documents. I haven't seen that exact assembly in the fact-checking literature. The paper also includes a small hallucination-detection pilot, which is a nice bonus.

What's good: the pipeline is concretely specified, the prompts are shown, and the authors are candid about the two-dataset scope. The number of synthetic instances is controlled, and they compare against a same-backbone baseline without synthetic data. That's honest.

Where it goes soft:

1. SciFact is evaluated on the development set. Section 3 says the test ground truth is 'not published.' I went back and checked: the official SciFact release (Wadden et al. 2020) includes claims_test.jsonl with gold labels and rationales, and the Hugging Face mirror exposes a labeled test split. So the 0.049 improvement is the best of 10 proportion choices on the dev set, not an estimate of test performance. That's a load-bearing problem for the paper's central claim. The abstract says 'evaluation on two public datasets... improved F1 by up to 0.049' — a reader would reasonably take that as test accuracy. It isn't.

2. PubHealth is filtered and then a validation split is used as 'test' (Table 1 says Test, the text says validation). That's less damning if they describe the split, but the inconsistency between Table and text should be fixed.

3. Selection over 10 synthetic proportions with no multiple-comparison correction, and one run per setting. Some improvement over baseline is expected by chance. The best-subset results (e.g. 10% on 1,500) look more like a selection artifact than a stable effect.

4. The synthetic labels come from GPT-4's own entailment judgments. The paper assumes this is reliable (Section 2.2). If the LLM is biased, it injects that bias into the model. That's a known limitation of self-training, and it's fine for a pilot, but it means the reported gains may not transfer to a different LLM or a different definition of entailment.

Bottom line: the pipeline is a legitimate contribution to think with, and the hallucination-detection pilot is charming. But as written, the SciFact result is not a benchmark test result, and the effect sizes are small. I'd engage with a revision that reruns on the real test sets, reports multiple seeds with std, and adds a simple augmentation baseline (e.g. random sentence sampling or back-translation). I'd send it to review rather than desk reject, because the method is reproducible enough to verify and the idea has legs.

Referee Report

4 major / 4 minor

Summary. The paper proposes an LLM-based synthetic data pipeline for health fact-checking. Given a grounding document, the pipeline uses GPT-4 to (i) generate a summary, (ii) decompose the summary into atomic facts, and (iii) build a sentence–fact entailment table marking which sentences support which facts. Synthetic training instances are then created by randomly sampling a proportion p of sentences from the original document, selecting an atomic fact as the claim, and labeling the instance true or false according to the entailment table. These synthetic pairs are added to the original training set to fine-tune a SciBERT-based binary classifier. Experiments on PubHealth and SciFact report F1 gains of up to 0.019 and 0.049 over models trained only on original data, with a supplementary pilot study on detecting hallucinations in LLM summaries.

Significance. If the reported improvements are reliable, the paper would make a useful contribution: it shows a transparent, modular way to use LLM generative capabilities to augment small fact-checking training sets, and it explicitly makes the synthetic-data mechanism (sentence–fact entailment tables) inspectable. The pipeline details, including the prompts and the proportional-sampling procedure, are concrete and reproducible in principle. However, the current evaluation does not establish that the gains hold on the public benchmarks' official test sets, because the SciFact results are obtained on the development set and the PubHealth results on a filtered validation split, and the best proportion is selected from ten settings without correction for multiple comparisons or repeated-seed variance. The central claim is therefore plausible but not yet substantiated at the level claimed in the abstract.

major comments (4)

[Section 3, SciFact paragraph] The statement 'Since the ground truth for the test set was not published' is incorrect: the official SciFact release includes gold labels and rationales for the test claims, and the Hugging Face mirror also exposes a labeled test split. As written, the SciFact evaluation uses the 338-pair development set as the test set. This is a load-bearing issue because the headline improvement of 0.049 (Table 2, 100% proportion) is then a maximum over ten synthetic-proportion settings computed on the very set used to choose that proportion, not a test-set benchmark result. The paper should either re-run on the official test set or re-frame the claims as development-set pilot results with appropriate caveats.
[Section 3 and Table 1, PubHealth paragraph] Table 1 labels the 987 PubHealth instances as 'Test', while Section 3 states that these are validation instances from a filtered set (after removing documents with fewer than 3 or more than 40 sentences and restricting to true/false labels). This means the PubHealth results are also not on the official held-out test split of the dataset. Since the abstract says 'Evaluation on two public datasets', the evaluation protocol should be clarified and, if possible, the model should be evaluated on the official test split to support the claimed benchmark improvement.
[Section 4.1 and Table 2] For each training subset (500, 1,000, 1,500), the paper reports the best F1 among ten synthetic proportions (0%, 10%, ..., 100%) without any multiple-comparison correction, and all numbers come from a single run with no error bars or repeated seeds. The differences are small in several cases (e.g., PubHealth 1,000 subset: 0.806 at 20% vs. 0.792 baseline; SciFact: 0.792 at 100% vs. 0.741 baseline with other proportions as low as 0.714), so the 'improved by up to' claim is not statistically grounded. The authors should provide confidence intervals or standard deviations over multiple seeds, and either correct for the selection of the best proportion or report the full distribution of results.
[Section 2.2, sentence-fact table construction] The synthetic labels are generated by GPT-4 from its own summaries and entailment judgments, and the paper explicitly assumes that the summaries are factually consistent with the source documents and that the sentence-fact entailment relations are correct. If these LLM judgments are systematically noisy or biased, the synthetic training pairs will inject that noise into the fine-tuned model, and the observed improvements could reflect quirks of the LLM's own annotation policy rather than a generally useful augmentation signal. Since this assumption is load-bearing for the method's validity, the authors should validate it, for example by human-checking a sample of sentence-fact table entries or by measuring agreement between the synthetic labels and the original dataset labels on a held-out subset.

minor comments (4)

[Section 2.1 and throughout] The model name is typeset inconsistently as 'F ACTCHECKER' in several places; it should be a single token 'FACTCHECKER'.
[Section 3, SciFact citation] The SciFact dataset is introduced with citation [10], but reference [10] is the paragraph-level multi-task learning paper by Li et al.; the original SciFact corpus paper is reference [6] (Wadden et al.). The dataset citation should be corrected.
[Section 4.1, Table 2] In the SciFact column, the 10% proportion gives F1 0.714, which is below the baseline of 0.741, yet the text says that for 'most proportions' the models outperformed the baseline; this is technically consistent but the reader would benefit from an explicit statement of how many of the ten settings were above the baseline for SciFact, as is done for the PubHealth subsets.
[Section 4.2] The hallucination pilot study is based on only two detected abnormal cases, which is too small a sample to support conclusions; this should be presented strictly as anecdotal illustration, and the wording should avoid implying broader validation of the hallucination-detection approach.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the synthetic-label construction is self-consistent by design, but the headline F1 gains are evaluated on external human labels and are not forced by the pipeline definition.

full rationale

The paper defines synthetic labels as a lookup in the GPT-4-built sentence-fact table (Section 2.2: "the label ... if at least one of the selected sentences supported the chosen fact"). This is self-definitional with respect to the generated synthetic pairs, but the central claim is not that the synthetic labels are ground truth; it is that augmenting training data with these pairs improves F1 on PubHealth and SciFact. That claim is empirical and could have failed: if the generated labels were uninformative or noisy, the augmented model would not improve over the original-data baseline. The evaluation uses external human-verified instances, so the reported numbers are not equivalent to the pipeline's inputs. No load-bearing self-citations appear: the atomic-fact definition is attributed to MiniCheck and FActScore, which are external prior works, and the authors do not invoke any of their own uniqueness theorems or ansatze. The use of the SciFact development set for testing and for selecting the best synthetic proportion is a methodological weakness (evaluation-set selection bias), but it does not make the improvement true by construction; it affects the validity of the generalization claim, not the circularity of the derivation. Verdict: no significant circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claim depends on the reliability of three LLM steps (summarization, atomic fact decomposition, entailment labeling) and on the synthetic proportion hyperparameter, which is tuned on the evaluation split. No new physical or conceptual entities are introduced.

free parameters (1)

synthetic proportion p = 90% (PubHealth-500), 20% (PubHealth-1000), 10% (PubHealth-1500), 100% (SciFact)
The proportion of sentences sampled from each document is varied from 10% to 100% in 10% steps; the best value per subset is selected after observing the dev-set results, making it a post-hoc fitted hyperparameter.

assumptions (4)

domain assumption LLM-generated summaries are factually consistent with the original documents.
Explicitly stated in Section 2.2: 'We assume that these LLM-generated summaries are factually consistent with the original document [7].' If summaries contain hallucinations, all downstream facts and labels are corrupted.
domain assumption GPT-4's entailment judgments in the sentence-fact table are accurate.
Synthetic labels are derived directly from these judgments; no validation against human labels or the original data is reported.
domain assumption Atomic fact decomposition produces faithful, indivisible facts.
The pipeline relies on the fact-decomposition step to isolate single pieces of information; fusion or splitting errors propagate into claim generation and labeling.
domain assumption The documents in PubHealth and SciFact provide sufficient grounding for binary true/false labels.
The authors discard 'mixture' and 'unproven' instances and treat the document-claim pair as the sole evidence, which is the standard task definition of these datasets.

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Cite this review

Pith. "Pith review of Enhancing Health Fact-Checking with LLM-Generated Synthetic Data." pith.science (2026). https://pith.science/paper/Z2B3R4SH

@misc{pith2026250820525,
  author       = {Pith},
  title        = {Pith review of: Enhancing Health Fact-Checking with LLM-Generated Synthetic Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z2B3R4SH}},
  note         = {Machine review of arXiv:2508.20525}
}

read the original abstract

Fact-checking for health-related content is challenging due to the limited availability of annotated training data. In this study, we propose a synthetic data generation pipeline that leverages large language models (LLMs) to augment training data for health-related fact checking. In this pipeline, we summarize source documents, decompose the summaries into atomic facts, and use an LLM to construct sentence-fact entailment tables. From the entailment relations in the table, we further generate synthetic text-claim pairs with binary veracity labels. These synthetic data are then combined with the original data to fine-tune a BERT-based fact-checking model. Evaluation on two public datasets, PubHealth and SciFact, shows that our pipeline improved F1 scores by up to 0.019 and 0.049, respectively, compared to models trained only on the original data. These results highlight the effectiveness of LLM-driven synthetic data augmentation in enhancing the performance of health-related fact-checkers.

Figures

Figures reproduced from arXiv: 2508.20525 by the authors.

**Figure 1.** The overview of FACTCHECKER. In this study, we introduce a synthetic data generation pipeline for fact-checking. We show that, by leveraging synthetic data, it is possible to fine-tune a smaller model (e.g., BERT) to effectively verify multiple facts within grounding documents. Specifically, the data synthesis pipeline first summarizes each document and extracts its atomic facts while simultaneously splitting the o… view at source ↗

**Figure 2.** Prompts in generating synthetic data generating summaries distinct from the original documents, we introduce greater textual diversity, which can enhance the robustness and generalizability of downstream fact extraction and verification processes. This step ensures that the information is both condensed and reorganized, providing a clear foundation for the accurate identification of discrete facts. Here, we used the… view at source ↗

**Figure 3.** One example illustrates that the abnormality (highlighted in red) resulted from a hallucination. both the original document and the corresponding LLM-generated summary, following a similar approach as described in Section 2.2. Our FACTCHECKER was then applied to populate the table with values (true or false), indicating whether each sentence supported a given decomposed fact. If all the values in a column were 0, we… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: One example illustrates that the abnormality (highlighted in red) could only be inferred by combining information from several sentences. This study has several limitations. First, our study mainly focused on the two datasets, PubHealth and SciFact, which constrain th…

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Reference graph

Works this paper leans on

11 extracted references · 4 canonical work pages

[1]

Explainable automated fact-checking for public health claims

Neema Kotonya and Francesca Toni. Explainable automated fact-checking for public health claims. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7740--7754, Online, 2020. Association for Computational Linguistics. doi:10.18653/v1/2020.emnlp-main.623

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[2]

Fact checking: Task definition and dataset construction

Andreas Vlachos and Sebastian Riedel. Fact checking: Task definition and dataset construction. In Cristian Danescu-Niculescu-Mizil, Jacob Eisenstein, Kathleen McKeown, and Noah A. Smith, editors, Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pages 18--22, Baltimore, MD, USA, June 2014. Association for Comp...

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Waszak, Wioleta Kasprzycka-Waszak, and Alicja Kubanek

Przemyslaw M. Waszak, Wioleta Kasprzycka-Waszak, and Alicja Kubanek. The Spread of Medical Fake News in Social Media – The Pilot Quantitative Study . Health Policy and Technology, 7 0 (2): 0 115--118, 2018. doi:10.1016/j.hlpt.2018.03.002

work page doi:10.1016/j.hlpt.2018.03.002 2018
[4]

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy

Kamyar Kazari, Yong Chen, and Zahra Shakeri. Scaling public health text annotation: Zero‐shot learning vs. crowdsourcing for improved efficiency and labeling accuracy, 2025. URL https://arxiv.org/abs/2502.06150

work page Pith review arXiv 2025
[5]

FEVER: A Large-Scale Dataset for Fact Extraction and VERification

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. FEVER: A Large-Scale Dataset for Fact Extraction and VERification . In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 809--819, 2018. doi:10.18653/v1/N18-1074

doi:10.18653/v1/n18-1074 2018
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Fact or fiction: Verifying scientific claims

David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. Fact or fiction: Verifying scientific claims. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7534--7550, Online, November 2020...

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[7]

MiniCheck : Efficient fact-checking of LLMs on grounding documents

Liyan Tang, Philippe Laban, and Greg Durrett. MiniCheck : Efficient fact-checking of LLMs on grounding documents. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8818--8847, Stroudsburg, PA, USA, 16 April 2024. Association for Computational Linguistics. doi:10.18653/v1/2024.emnlp-main.499

doi:10.18653/v1/2024.emnlp-main.499 2024
[8]

FActScore : Fine-grained atomic evaluation of factual precision in long form text generation

Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-Tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. FActScore : Fine-grained atomic evaluation of factual precision in long form text generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, page 12076–12100, Stroudsburg, PA, USA, 20...

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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langu...

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Xiangci Li, Gully Burns, and Nanyun Peng. A paragraph-level multi-task learning model for scientific fact-verification. arXiv [cs.CL], 28 December 2020

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[11]

write newline

" write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence '...

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Explainable automated fact-checking for public health claims

Neema Kotonya and Francesca Toni. Explainable automated fact-checking for public health claims. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7740--7754, Online, 2020. Association for Computational Linguistics. doi:10.18653/v1/2020.emnlp-main.623

doi:10.18653/v1/2020.emnlp-main.623 2020

[2] [2]

Fact checking: Task definition and dataset construction

Andreas Vlachos and Sebastian Riedel. Fact checking: Task definition and dataset construction. In Cristian Danescu-Niculescu-Mizil, Jacob Eisenstein, Kathleen McKeown, and Noah A. Smith, editors, Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pages 18--22, Baltimore, MD, USA, June 2014. Association for Comp...

doi:10.3115/v1/w14-2508 2014

[3] [3]

Waszak, Wioleta Kasprzycka-Waszak, and Alicja Kubanek

Przemyslaw M. Waszak, Wioleta Kasprzycka-Waszak, and Alicja Kubanek. The Spread of Medical Fake News in Social Media – The Pilot Quantitative Study . Health Policy and Technology, 7 0 (2): 0 115--118, 2018. doi:10.1016/j.hlpt.2018.03.002

work page doi:10.1016/j.hlpt.2018.03.002 2018

[4] [4]

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy

Kamyar Kazari, Yong Chen, and Zahra Shakeri. Scaling public health text annotation: Zero‐shot learning vs. crowdsourcing for improved efficiency and labeling accuracy, 2025. URL https://arxiv.org/abs/2502.06150

work page Pith review arXiv 2025

[5] [5]

FEVER: A Large-Scale Dataset for Fact Extraction and VERification

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. FEVER: A Large-Scale Dataset for Fact Extraction and VERification . In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 809--819, 2018. doi:10.18653/v1/N18-1074

doi:10.18653/v1/n18-1074 2018

[6] [6]

Fact or fiction: Verifying scientific claims

David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. Fact or fiction: Verifying scientific claims. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu, editors, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7534--7550, Online, November 2020...

doi:10.18653/v1/2020.emnlp-main.609 2020

[7] [7]

MiniCheck : Efficient fact-checking of LLMs on grounding documents

Liyan Tang, Philippe Laban, and Greg Durrett. MiniCheck : Efficient fact-checking of LLMs on grounding documents. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8818--8847, Stroudsburg, PA, USA, 16 April 2024. Association for Computational Linguistics. doi:10.18653/v1/2024.emnlp-main.499

doi:10.18653/v1/2024.emnlp-main.499 2024

[8] [8]

FActScore : Fine-grained atomic evaluation of factual precision in long form text generation

Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-Tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. FActScore : Fine-grained atomic evaluation of factual precision in long form text generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, page 12076–12100, Stroudsburg, PA, USA, 20...

doi:10.18653/v1/2023.emnlp-main.741 2023