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REVIEW 4 major objections 6 minor 40 references

Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Large language models, given web search snippets and a one-shot prompt, can fact-check real-world claims as accurately as fine-tuned small classifiers, and larger models benefit most from the evidence.

desk verdict Solid baseline study for LLM fact-checking, but the evidence condition leaks post-hoc verdicts into the retrieval snippets, and the abstract oversells small-model parity. read the letter →

arxiv 2502.08909 v1 pith:3Q7ONQ3C submitted 2025-02-13 cs.CL cs.AI

Premtim Sahitaj , Iffat Maab , Junichi Yamagishi , Jawan Kolanowski , Sebastian Möller , Vera Schmitt This is my paper · ORCID
classification cs.CLcs.AI
keywords automatedfact-checkinglargelanguagemodelsretrieval-augmentedgenerationPolitiFactTIGERScorelabelschemesone-shotpromptingevidenceintegration
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

This paper tries to establish a baseline for automated fact-checking with large language models: given a real-world claim and a handful of web search snippets, a one-shot prompt that asks the model to reason, classify, and explain can yield accurate verdicts and useful justifications without fine-tuning. Using 17,856 PolitiFact claims and Llama-3 models of 3B, 8B, and 70B parameters, the authors find that larger models consistently outperform smaller ones on both classification accuracy and justification quality. They also find that adding retrieved evidence improves performance for every model, and that the largest models benefit the most, contrary to the expectation that smaller models would gain more from external knowledge. The result matters because it suggests a feasible path for practical fact-checking assistance: open-weight LLMs with retrieval, rather than costly fine-tuned systems, can provide transparent, explainable verdicts on real-world claims.

What carries the argument

The central object is a one-shot, retrieval-augmented prompting framework with constrained decoding. For each claim, the model receives a system prompt that defines the labeling scheme, a user message containing the speaker, context, claim, and (when used) the top-10 web search snippets, and a single assistant example; the model must output a JSON object with 'reasoning', 'verdict', and 'explanation'. The framework's work is carried by the combination of chain-of-thought reasoning, structured output enforcement via constrained generation, and the reference-free TIGERScore metric (which assigns penalty scores to errors in the generated justification) for evaluation.

What would settle it

Compute a held-out correlation between the words in the retrieved snippets and the model's predicted verdict: if, for example, snippets for claims labeled 'False' frequently contain the word 'false' or 'debunk' despite the filter, and models that see those snippets predict 'False' more often than models that do not, the evidence-benefit result would be an artifact of label leakage rather than genuine evidence use. A cleaner test would compare evidence-augmented accuracy against a control where snippets are replaced by unrelated web pages.

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Extended reading notes

Core claim

The central claim is that retrieval-augmented, few-shot inference with LLMs provides a viable and transparent alternative to fine-tuned small models for automated fact-checking of real-world claims. On a dataset of 17,856 PolitiFact claims, the authors evaluate Llama-3 instruction-tuned models at three sizes under binary, three-class, and five-class labeling schemes, prompting each model to produce a step-by-step analysis, a verdict, and a natural-language explanation in a structured JSON format. Across all schemes, the 70B models beat the 8B and 3B models, and the 3.3-70B model reaches a binary F1 of 0.747 with evidence, surpassing the fine-tuned ModernBERT classifier's 0.718. The paper also reports that evidence retrieval (top-10 web snippets with fact-checking sites filtered out) significantly improves both classification and justification quality for all models, with the largest models showing the largest gains. Distinguishing fine-grained labels remains hard, and the authors reject the hypothesis that justification quality degrades with label complexity.

Load-bearing premise

The load-bearing premise is that the top-10 web snippets retrieved from a single search API provide sufficient, unbiased evidence for each claim and do not indirectly reveal the official fact-check verdict; if snippets leak the answer, the measured benefit of evidence integration is inflated.

Editorial extensions

If this is right

  • If the central claim holds, automated fact-checking systems can be built on open-weight LLMs with no task-specific fine-tuning, reducing development cost and allowing rapid adaptation to new claims.
  • Evidence integration is the most reliable performance lever: adding top-10 web snippets improves classification and justification quality across every model size and label scheme, so future systems should invest in retrieval quality.
  • Larger models are the right target for retrieval-augmented fact-checking: the 70B models benefit most from evidence and surpass fine-tuned small classifiers, so compute spent on scale pays off in this task.
  • Fine-grained five-class verdicts are near the limit of what current LLMs can reliably distinguish, so practical deployments may need coarser schemes or additional evidence types.
  • The ModernBERT fine-tuning results confirm that evidence is the most informative input component, ahead of speaker identity and context, so claim-evidence alignment should be a design focus.

Reading between the lines

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

  • The paper's evidence-leakage filter is imperfect; an extension that audits snippets for verdict-related wording could determine how much of the evidence benefit is genuine grounding rather than label leakage.
  • The framework's structured reasoning-plus-verdict-plus-explanation output could be adopted as an evaluation protocol for other real-world claim datasets, not just PolitiFact, since it does not rely on dataset-specific fine-tuning.
  • Because the authors find evidence helps most when labels are coarse, a natural next step is adaptive label granularity: use fine labels when evidence is strong and fall back to coarse labels when it is weak.
  • The 667 claims for which no evidence was retrieved could serve as a natural stress test for whether models know when to abstain, since the framework currently forces a verdict even without evidence.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes a one-shot LLM-based framework for automated fact-checking that jointly produces a step-by-step reasoning trace, a veracity verdict, and a natural-language explanation. Using 17,856 PolitiFact claims (2007–2024), it compares Llama-3.2-3B, Llama-3.1-8B, Llama-3.1-70B, and Llama-3.3-70B under binary, three-class, and five-class label schemes, in settings with and without web evidence retrieved via the Serper API, and additionally fine-tunes ModernBERT-large as an upper-bound classifier. The main reported findings are that evidence integration improves performance across all models, larger models benefit most from evidence, larger LLMs surpass fine-tuned SLMs, and smaller LLMs are comparable to fine-tuned SLMs in some settings.

Significance. If the findings hold, the paper provides a useful baseline for retrieval-augmented automated fact-checking with open-weight LLMs, together with a structured output format for transparent justifications. The study's strengths include its large corpus, multiple label granularities, comparison across model scales from the same architecture family, structured decoding via vLLM/outlines, the use of TIGERScore for justification evaluation, and statistical testing of the hypotheses. The central evidence-integration claim, however, is endangered by temporal label leakage in the retrieval protocol, and Table 6 contains internally inconsistent classification metrics. These issues must be resolved before the headline conclusions can be accepted.

major comments (4)
  1. [§4.5] The evidence-retrieval design is vulnerable to temporal label leakage. The claims span 2007–2024, but snippets are retrieved at experiment time via Serper, so the current web index can return post-hoc articles, corrections, and news coverage that disclose the PolitiFact verdict. The filter removes known fact-checking sites and keywords such as 'PolitiFact', 'fact-check', and 'debunk', but it does not remove general news reports, court documents, or commentary that restate the verdict; even the example in Table 3 includes an Aug 16, 2017 snippet stating that 'The Times subsequently issued a correction'. Because hypotheses H3 and H4 rest on the size of the evidence benefit, the reported improvements may partly reflect the model reading the answer from the snippet rather than verifying the claim. I ask for a leakage audit: sample a few hundred claims, annotate whether the retrieved snippets contain verdict-bearing information (explicit labels, 'false', 'corrected', 'no evidence', 'pants on fire', etc.), and report the evidence-condition results on the subset free of such leakage; alternatively, use temporally constrained retrieval with snapshots dated before the fact-checking ruling and rerun the main comparisons.
  2. [Table 6] The binary classification metrics in Table 6 are mutually inconsistent as labeled. For single-label predictions, micro-F1 equals accuracy, yet entries such as F1_micro=0.624 with Accuracy=0.504 for 3.2-3B-Instruct without evidence cannot both be correct as labeled; the same pattern occurs in every binary row. This makes the binary results and the abstract's claim about smaller LLMs being comparable to fine-tuned SLMs difficult to interpret: with the accuracy column, the 3.2-3B model with evidence (0.557) is far below ModernBERT with evidence (0.718), whereas with the F1 columns the gap is only 0.647 vs 0.696. Please recompute and clearly define all metrics, and state which numbers support the claims in the abstract and §5.3.
  3. [§5.3, Table 7] The fine-tuning setup for ModernBERT-large is not specified. The manuscript does not report the train/validation/test split, hyperparameters (learning rate, batch size, number of epochs, early stopping, seed averaging), or whether the split is performed by claim or by speaker/context to prevent near-duplicate leakage. It also does not state how evidence snippets are concatenated and truncated to 8,192 tokens, nor whether the 667 claims without retrieved evidence are dropped from all conditions. Without these details, the claimed upper-bound comparison between one-shot LLMs and fine-tuned SLMs is not reproducible, and the conclusion that larger LLMs 'consistently surpass' fine-tuned SLMs is not fully established.
  4. [§5.3] The statistical tests are underspecified. The paired t-tests for the evidence benefit do not state the pairing unit (per claim, per model, or per label scheme) or the number of comparisons, and pooling across models and label schemes would violate independence and inflate significance. The Friedman tests similarly mix model size and evidence conditions without a clear repeated-measures design. Please report the exact test design, sample sizes, effect sizes, and multiple-comparison corrections for the large number of comparisons in Tables 4–6.
minor comments (6)
  1. [Abstract and §5.3] The statement that smaller LLMs provide comparable task performance to fine-tuned SLMs should be qualified by label scheme: the 3-class and 5-class results are indeed comparable, but the binary accuracy gap in Table 6 (0.557 vs 0.718 for 3.2-3B and ModernBERT with evidence) is substantial.
  2. [§4.4] The description of label aggregation is confusing: 'the original labels true and false are grouped into mostly true and mostly false, respectively' should read 'true is merged into mostly true, and false is merged into mostly false'. The binary merging of half-true into mostly true is described correctly.
  3. [§5.2] There are cross-reference errors: the claim example appears in Figure 1 and the snippets in Table 3, but the text refers to 'Table 1' and 'Table 2' for these; 'The output in Table 2 demonstrates' should be 'The output in Figure 2 demonstrates'.
  4. [§4.5] The list of excluded fact-checking sites and the exact keyword filter are not provided; please include them so that the leakage mitigation can be assessed and reproduced.
  5. [§4.6] The paper relies on TIGERScore as a reference-free metric for justification quality, but it does not validate TIGERScore against human judgments for fact-checking justifications. Since H2 and parts of H3/H4 depend on this metric, a small human-correlation study or sensitivity analysis would strengthen the conclusions.
  6. [General] No code, prompts, retrieved evidence, or model outputs are released, which hinders reproducibility of this otherwise empirical study. Please consider making these available.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical benchmark study whose claims rest on measured accuracy, F1, and an external reference-free metric; no result is forced by construction or by self-citation.

full rationale

The paper is an empirical evaluation rather than a derivation. It collects 17,856 PolitiFact claims, retrieves top-10 web snippets, prompts Llama-3 models of different sizes with and without evidence, and reports classification accuracy/F1 and TIGERScore for justification quality. No parameter is fitted to the target labels and then renamed as a prediction: the fine-tuned ModernBERT models are explicitly used as upper-bound baselines, and they are evaluated on the same held-out data rather than used to produce the LLM outputs. The evidence benefit, model-size benefit, and label-granularity findings all come from paired comparisons of measured model outputs, so they are not true by definition. The only mildly self-referential element is using TIGERScore, a 13B LLM-based metric, to grade LLM-generated justifications; however, TIGERScore is an externally built metric (Jiang et al., 2024), is not fine-tuned on this paper's data, and does not supply the classification results. The evidence-retrieval design has a genuine external validity risk, which the authors themselves acknowledge in Section 4.5 when they say they filter fact-checking sites and keywords 'to reduce information leaking in from pages reporting the actual verification results'; such leakage, if present, would inflate measured performance, but it is a validity concern rather than a circularity concern because the evidence condition's contribution is not constructed from the ground-truth labels. There are no load-bearing self-citations, no uniqueness claims imported from the authors' prior work, and no renamed known result. The derivation chain is therefore self-contained as an empirical study.

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

The paper introduces no free parameters and no new entities. It relies on domain assumptions about data quality, evidence sufficiency, and the validity of the evaluation metric. These are reasonable for an empirical baseline study but are not independently verified.

assumptions (4)
  • domain assumption PolitiFact labels are reliable ground truth for claim veracity.
    The dataset is built from PolitiFact's expert ratings, and the paper treats these labels as correct without independent verification. This enters throughout the evaluation.
  • domain assumption Top-10 web search snippets from a single API provide sufficient and unbiased evidence for each claim.
    Section 4.5 describes the evidence retrieval. The paper assumes these snippets are relevant and do not leak the verdict, which is structurally distinct from the central claim and load-bearing for the evidence results.
  • domain assumption TIGERScore is a valid reference-free measure of justification quality.
    Section 4.6 uses TIGERScore without validating it against human judgments for this task. All justification quality findings depend on this assumption.
  • domain assumption Running each task three times and taking the majority vote gives a stable classification estimate.
    Section 4.6 reports majority-vote results, but no variance or confidence intervals are shown, so stability is assumed rather than demonstrated.

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Cite this review

Pith. "Pith review of Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs." pith.science (2026). https://pith.science/paper/3Q7ONQ3C

@misc{pith2026250208909,
  author       = {Pith},
  title        = {Pith review of: Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3Q7ONQ3C}},
  note         = {Machine review of arXiv:2502.08909}
}
read the original abstract

Fact-checking is necessary to address the increasing volume of misinformation. Traditional fact-checking relies on manual analysis to verify claims, but it is slow and resource-intensive. This study establishes baseline comparisons for Automated Fact-Checking (AFC) using Large Language Models (LLMs) across multiple labeling schemes (binary, three-class, five-class) and extends traditional claim verification by incorporating analysis, verdict classification, and explanation in a structured setup to provide comprehensive justifications for real-world claims. We evaluate Llama-3 models of varying sizes (3B, 8B, 70B) on 17,856 claims collected from PolitiFact (2007-2024) using evidence retrieved via restricted web searches. We utilize TIGERScore as a reference-free evaluation metric to score the justifications. Our results show that larger LLMs consistently outperform smaller LLMs in classification accuracy and justification quality without fine-tuning. We find that smaller LLMs in a one-shot scenario provide comparable task performance to fine-tuned Small Language Models (SLMs) with large context sizes, while larger LLMs consistently surpass them. Evidence integration improves performance across all models, with larger LLMs benefiting most. Distinguishing between nuanced labels remains challenging, emphasizing the need for further exploration of labeling schemes and alignment with evidences. Our findings demonstrate the potential of retrieval-augmented AFC with LLMs.

Figures

Figures reproduced from arXiv: 2502.08909 by the authors.

Figure 1
Figure 1. Example data point of a statement made by the New York Times Editorial Board and evaluated by PolitiFact as False. PolitiFact’s rating system follows an ordinal six-class labeling scheme [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Analysis of the New York Times editorial case involving Sarah Palin. The output in [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

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Reference graph

Works this paper leans on

40 extracted references · 19 canonical work pages

  1. [1]

    Lewandowsky, J

    S. Lewandowsky, J. Cook, D. Lombardi, Debunking Handbook 2020, 2020. doi: 10.17910/B7. 1182

    work page doi:10.17910/b7 2020

  2. [2]

    Vlachos, S

    A. Vlachos, S. Riedel, Fact Checking: Task definition and dataset construction, in: C. Danescu- Niculescu-Mizil, J. Eisenstein, K. McKeown, N. A. Smith (Eds.), Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, Association for Compu- tational Linguistics, Baltimore, MD, USA, 2014, pp. 18–22. doi:10.3115/v1/W14-2508

    doi:10.3115/v1/w14-2508 2014

  3. [3]

    Hassan, F

    N. Hassan, F. Arslan, C. Li, M. Tremayne, Toward Automated Fact-Checking: Detecting Check- worthy Factual Claims by ClaimBuster, in: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Halifax NS Canada, 2017, pp. 1803–

    work page 2017

  4. [4]

    Z. Guo, M. Schlichtkrull, A. Vlachos, A Survey on Automated Fact-Checking, Transactions of the Association for Computational Linguistics 10 (2022) 178–206. doi:10.1162/tacl_a_00454

    doi:10.1162/tacl_a_00454 2022

  5. [5]

    Nyhan, Facts and Myths about Misperceptions, Journal of Economic Perspectives 34 (2020) 220–236

    B. Nyhan, Facts and Myths about Misperceptions, Journal of Economic Perspectives 34 (2020) 220–236. doi:10.1257/jep.34.3.220

    work page doi:10.1257/jep.34.3.220 2020

  6. [6]

    X. Zhou, R. Zafarani, A Survey of Fake News: Fundamental Theories, Detection Meth- ods, and Opportunities, ACM Computing Surveys 53 (2021) 1–40. doi: 10.1145/3395046. arXiv:1812.00315

    arXiv 2021

  7. [7]

    Graves, Understanding the promise and limits of automated fact-checking, 2018

    L. Graves, Understanding the promise and limits of automated fact-checking, 2018

    work page 2018

  8. [8]

    P. B. Brandtzaeg, A. Følstad, Trust and distrust in online fact-checking services, Communications of the ACM 60 (2017) 65–71. doi:10.1145/3122803

    work page doi:10.1145/3122803 2017

Show all 40 references
  1. [9]

    V. Setty, Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking over Larger Language Models, in: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Washington DC USA, 2024, pp. 2842–2846. doi:10. 114...

    2024

  2. [10]

    Vaswani, N

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention Is All You Need, 2023. doi:10.48550/arXiv.1706.03762. arXiv:1706.03762

    2023 doi arXiv

  3. [11]

    Devlin, M.-W

    J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of Deep Bidirec- tional Transformers for Language Understanding, 2019. doi: 10.48550/arXiv.1810.04805. arXiv:1810.04805

    2019 doi arXiv

  4. [12]

    Radford, K

    A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, Improving Language Understanding by Generative Pre-Training (2018)

    2018

  5. [13]

    C. V. Nguyen, X. Shen, R. Aponte, Y. Xia, S. Basu, Z. Hu, J. Chen, M. Parmar, S. Kunapuli, J. Barrow, J. Wu, A. Singh, Y. Wang, J. Gu, F. Dernoncourt, N. K. Ahmed, N. Lipka, R. Zhang, X. Chen, T. Yu, S. Kim, H. Deilamsalehy, N. Park, M. Rimer, Z. Zhang, H. Yang, R. A. Rossi, T...

    2024 doi arXiv

  6. [14]

    Kaplan, S

    J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, D. Amodei, Scaling Laws for Neural Language Models, 2020. arXiv:2001.08361

    2020 arXiv

  7. [15]

    Ouyang, J

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, R. Lowe, Training language models to follow instructions with...

    2022 arXiv

  8. [16]

    Rafailov, A

    R. Rafailov, A. Sharma, E. Mitchell, S. Ermon, C. D. Manning, C. Finn, Direct Preference Optimiza- tion: Your Language Model is Secretly a Reward Model, 2023. arXiv:2305.18290

    2023 arXiv

  9. [17]

    Schulhoff, M

    S. Schulhoff, M. Ilie, N. Balepur, K. Kahadze, A. Liu, C. Si, Y. Li, A. Gupta, H. Han, S. Schulhoff, P. S. Dulepet, S. Vidyadhara, D. Ki, S. Agrawal, C. Pham, G. Kroiz, F. Li, H. Tao, A. Srivastava, H. Da Costa, S. Gupta, M. L. Rogers, I. Goncearenco, G. Sarli, I. Galynker, D....

    2024 arXiv

  10. [18]

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. ...

    2020 arXiv

  11. [19]

    Lewis, E

    P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, D. Kiela, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2021. doi:10.48550/arXiv.2005.11401. arXiv:2005.11401

    2021 doi arXiv

  12. [20]

    Ferreira, A

    W. Ferreira, A. Vlachos, Emergent: A novel data-set for stance classification, in: K. Knight, A. Nenkova, O. Rambow (Eds.), Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Associati...

    2016 doi

  13. [21]

    Augenstein, C

    I. Augenstein, C. Lioma, D. Wang, L. Chaves Lima, C. Hansen, C. Hansen, J. G. Simonsen, MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims, in: Proceedings of 2019 EMNLP-IJCNLP, Association for Computational Linguistics, 2019, pp. 4685–4697

    2019

  14. [22]

    Kotonya, F

    N. Kotonya, F. Toni, Explainable Automated Fact-Checking: A Survey, 2020

    2020

  15. [23]

    Russo, S

    D. Russo, S. S. Tekiroğlu, M. Guerini, Benchmarking the Generation of Fact Checking Explanations, Transactions of the Association for Computational Linguistics 11 (2023) 1250–1264

    2023

  16. [24]

    Maynez, S

    J. Maynez, S. Narayan, B. Bohnet, R. McDonald, On Faithfulness and Factuality in Abstractive Summarization, in: D. Jurafsky, J. Chai, N. Schluter, J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Comp...

    2020 doi

  17. [25]

    C. Si, N. Goyal, S. T. Wu, C. Zhao, S. Feng, H. Daumé III, J. Boyd-Graber, Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong, 2024

    2024

  18. [26]

    Eldifrawi, S

    I. Eldifrawi, S. Wang, A. Trabelsi, Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches, 2024. arXiv:2407.12853

    2024 arXiv

  19. [27]

    L. Pan, X. Wu, X. Lu, A. T. Luu, W. Y. Wang, M.-Y. Kan, P. Nakov, Fact-Checking Complex Claims with Program-Guided Reasoning, in: A. Rogers, J. Boyd-Graber, N. Okazaki (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Lo...

    2023

  20. [28]

    H. Wang, K. Shu, Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models, in: H. Bouamor, J. Pino, K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, 20...

    2023 doi

  21. [29]

    Liar, Liar Pants on Fire

    W. Y. Wang, “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection, in: R. Barzilay, M.-Y. Kan (Eds.), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguisti...

    2017 doi

  22. [30]

    B. M. Yao, A. Shah, L. Sun, J.-H. Cho, L. Huang, End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models, in: Proceedings of the 46th Interna- tional ACM SIGIR Conference on Research and Development in Information Retrieval, 2023, pp. 2...

    2023

  23. [31]

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, D. Zhou, Chain-of- Thought Prompting Elicits Reasoning in Large Language Models, 2023

    2023

  24. [32]

    Kotonya, F

    N. Kotonya, F. Toni, Towards a Framework for Evaluating Explanations in Automated Fact Verifi- cation, 2024. arXiv:2403.20322

    2024 arXiv

  25. [33]

    Zhang, K

    Z. Zhang, K. Rudra, A. Anand, Explain and Predict, and then Predict Again, in: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, WSDM ’21, Association for Computing Machinery, New York, NY, USA, 2021, pp. 418–426. doi:10.1145/3437963.3441758

    2021

  26. [34]

    Atanasova, J

    P. Atanasova, J. G. Simonsen, C. Lioma, I. Augenstein, Generating Fact Checking Explanations, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2020, pp. 7352–7364

    2020

  27. [35]

    B. T. Willard, R. Louf, Efficient Guided Generation for Large Language Models, 2023

    2023

  28. [36]

    Hanselowski, C

    A. Hanselowski, C. Stab, C. Schulz, Z. Li, I. Gurevych, A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking, 2019. doi: 10.48550/arXiv.1911.01214. arXiv:1911.01214

    2019 doi arXiv

  29. [37]

    Schlichtkrull, Z

    M. Schlichtkrull, Z. Guo, A. Vlachos, AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web, 2023. doi:10.48550/arXiv.2305.13117. arXiv:2305.13117

    2023 doi arXiv

  30. [38]

    Jiang, Y

    D. Jiang, Y. Li, G. Zhang, W. Huang, B. Y. Lin, W. Chen, TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks, 2024

    2024

  31. [39]

    Warner, A

    B. Warner, A. Chaffin, B. Clavié, O. Weller, O. Hallström, S. Taghadouini, A. Gallagher, R. Biswas, F. Ladhak, T. Aarsen, N. Cooper, G. Adams, J. Howard, I. Poli, Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finet...

    2024 doi arXiv

  32. [1812]

    doi:10.1145/3097983.3098131

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.