--- title: "Teaching Language Models to Check Grounded Claim Factuality" description: "The paper addresses checking whether a generated claim is supported by its source document, a key safeguard for… 42 references." authors: ["Yuxuan Ye", "Raul Santos-Rodriguez", "Edwin Simpson"] year: 2026 venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)" source: "https://aclanthology.org/2026.acl-long.1468/" doi: "10.18653/v1/2026.acl-long.1468" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/ye-2026-teaching-language-models-check-grounded/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ye-2026-teaching-language-models-check-grounded/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/51/) / Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies # Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies **Authors:** Yuxuan Ye, Raul Santos-Rodriguez and [Edwin Simpson](https://md.research.elamur.ai/free/en/authors/edwin-simpson/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1468/) · [PDF](https://aclanthology.org/2026.acl-long.1468.pdf) · [DOI 10.18653/v1/2026.acl-long.1468](https://doi.org/10.18653/v1/2026.acl-long.1468) · [OpenAlex](https://openalex.org/W7166828659) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary The paper addresses checking whether a generated claim is supported by its source document, a key safeguard for retrieval-augmented generation and other grounded LLM outputs. Entailment-classifier metrics require a decision threshold tuned for each dataset. Plain LLM-as-judge prompting leaves the model's reasoning unguided. The authors frame the problem as a True/False reading-comprehension exercise. Their two-step pipeline first splits a claim into atomic facts using a few-shot prompt adapted from Tang et al. (2024a). It then checks each fact against the document with a prompt modelled on strategies taught to language-test takers. This guided reasoning uses over 80% fewer tokens than open-ended reasoning. It is competitive across two benchmarks and achieves the best result reported to date on one of them. To lower inference cost further, the authors distil the pipeline into small language models in two phases. A teacher LLM's outputs are used for SFT, and DPO then contrasts the student's wrong answers with the teacher's correct ones. The resulting SLMs approach the performance of strong baselines while still producing readable rationales. ## Key points - **Benchmarks and models:** evaluation uses FacTax-Benchmark and LLM-AggreFact with balanced accuracy (BAcc). Experiments include Qwen3 4B and 30B-A3B models, and code is released at github.com/Haruhi07/Test-Taking. - **Pipeline ablation:** - The full Qwen3-30B-A3B pipeline reaches 78.0 BAcc on FacTax-Benchmark and 76.3 on LLM-AggreFact. - Removing both decomposition and the strategy prompt lowers these to 74.8 and 74.7. - For the 4B model, the full pipeline scores 73.0 and 75.6, versus 69.4 and 72.1 without either component. - **Two steps vs. one prompt:** the two-step pipeline beats a single-pass prompt combining both stages. For the 30B model, scores are 78.0 vs. 72.3 on FacTax-Benchmark. - **Robustness to wording:** prompts paraphrased by ChatGPT and Gemini give similar BAcc, so the strategy is not sensitive to exact phrasing. - **Training data matters for SLMs:** dropping the distillation data derived from one benchmark sharply hurts SFT performance on that benchmark. For example, LLM-AggreFact falls from 71.3 to 62.1, showing SLMs generalise less well than LLMs. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [University of Bristol](https://md.research.elamur.ai/free/en/universities/university-of-bristol/) — University, Bristol, United Kingdom · 18 works · 822 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,954 works · [🤝 Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · 626 works 🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works ## Cite Cite as: Yuxuan Ye, Raul Santos-Rodriguez and Edwin Simpson. 2026. Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{ye2026teaching, title = {{Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies}}, author = {Yuxuan Ye, Raul Santos-Rodriguez and Edwin Simpson}, booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, year = {2026}, doi = {10.18653/v1/2026.acl-long.1468}, url = {https://doi.org/10.18653/v1/2026.acl-long.1468} } ``` - [← previous Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling](https://md.research.elamur.ai/free/en/papers/pathmanathan-2026-teach-reward-model-correct-itself/) - [next → Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation](https://md.research.elamur.ai/free/en/papers/mule-2026-teaching-language-models-forecast-research/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/51/) ## References 42 references, 30 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Omaima Al-Kiyumi, Fawzia Al Seyabi, and Abdul Hamid Hassan. 2021. An empirical study on the effect of instruction on metacognitive strategies on efl reading comprehension: The case of foundation-level students in oman. International Education Studies, 14(8):30–42. [find](https://scholar.google.com/scholar?q=Omaima+Al%2DKiyumi%2C+Fawzia+Al+Seyabi%2C+and+Abdul+Hamid+Hassan%2E+2021%2E+An+empirical+study+on+the+effect+of+instruction+on+metacognitive+strategies+on+efl+reading+comprehension%3A+The+case+of+foundation%2Dlevel) 2. Meng Cao, Yue Dong, and Jackie Cheung. 2022. [Hallucinated but factual! inspecting the factuality of hallucinations in abstractive summarization](https://md.research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3340–3354, Dublin, Ireland. Assoc… 3. Shuyang Cao and Lu Wang. 2021. [CLIFF: Contrastive learning for improving faithfulness and factuality in abstractive summarization](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6633–6649, Online and Punta Cana, Dominican Republic. Association for Co… 4. Hung-Ting Chen, Fangyuan Xu, Shane Arora, and Eunsol Choi. 2023. [Understanding retrieval augmentation for long-form question answering](https://md.research.elamur.ai/free/en/papers/chen-2023-understanding-retrieval-augmentation-long-form/). arXiv preprint arXiv:2310.12150. 5. DeepSeek-AI. 2025. [Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). Preprint, arXiv:2501.12948. 6. Daniel Deutsch, Tania Bedrax-Weiss, and Dan Roth. 2021. [Towards question-answering as an automatic metric for evaluating the content quality of a summary](https://md.research.elamur.ai/free/en/papers/deutsch-2021-question-answering-as-automatic-metric/). Transactions of the Association for Computational Linguistics, 9:774–789. 7. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, and 1 others. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv e-prints, pages arXiv–2407. 8. Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022. [QAFactEval: Improved QA-based factual consistency evaluation for summarization](https://md.research.elamur.ai/free/en/papers/fabbri-2022-qafacteval-improved-qa-based-factual/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pa… 9. Alexander R Fabbri, Wojciech Kryściński, Bryan Mc-Cann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021. Summeval: Re-evaluating summarization evaluation. Transactions of the Association for Computational Linguistics, 9:391–409. [find](https://scholar.google.com/scholar?q=Alexander+R+Fabbri%2C+Wojciech+Krys%CC%81cin%CC%81ski%2C+Bryan+Mc%2DCann%2C+Caiming+Xiong%2C+Richard+Socher%2C+and+Dragomir+Radev%2E+2021%2E+Summeval%3A+Re%2Devaluating+summarization+evaluation%2E+Transactions+of+the+Association+for) 10. Tao Feng, Yicheng Li, Chenglin Li, Hao Chen, Fei Yu, and Yin Zhang. 2024. [Teaching small language models reasoning through counterfactual distillation](https://md.research.elamur.ai/free/en/papers/feng-2024-teaching-small-language-models-reasoning/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5831–5842, Miami, Florida, USA. Association for Com… 11. Zorik Gekhman, Jonathan Herzig, Roee Aharoni, Chen Elkind, and Idan Szpektor. 2023. [TrueTeacher: Learning factual consistency evaluation with large language models](https://md.research.elamur.ai/free/en/papers/gekhman-2023-trueteacher-learning-factual-consistency-evaluation/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2053–2070, Singapore. Association for… 12. Tanya Goyal and Greg Durrett. 2021. [Annotating and modeling fine-grained factuality in summarization](https://md.research.elamur.ai/free/en/papers/goyal-2021-annotating-modeling-fine-grained-factuality/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1449–1462, Online. Association for Computatio… 13. Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020. [Deberta: Decoding-enhanced bert with disentangled attention](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/). arXiv preprint arXiv:2006.03654. 14. Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. 2024. [Ruler: What’s the real context size of your long-context language models?](https://md.research.elamur.ai/free/en/papers/hsieh-2024-ruler-s-real-context-size/) arXiv preprint arXiv:2404.06654. 15. Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, and Yue Zhang. 2020. [What have we achieved on text summarization?](https://md.research.elamur.ai/free/en/papers/huang-2020-what-have-we-achieved-text/) In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 446–469, Online. Association for Computational Linguistics… 16. Alon Jacovi, Yonatan Bitton, Bernd Bohnet, Jonathan Herzig, Or Honovich, Michael Tseng, Michael Collins, Roee Aharoni, and Mor Geva. 2024. [A chain-of-thought is as strong as its weakest link: A benchmark for verifiers of reasoning chains](https://md.research.elamur.ai/free/en/papers/jacovi-2024-chain-thought-is-as-strong/). In Proceedings of the 62nd Annual Meeting of the Association… 17. Philippe Laban, Tobias Schnabel, Paul N. Bennett, and Marti A. Hearst. 2022. SummaC: Re-visiting NLI-based models for inconsistency detection in summarization. Transactions of the Association for Computational Linguistics, 10:163–177. [find](https://scholar.google.com/scholar?q=Philippe+Laban%2C+Tobias+Schnabel%2C+Paul+N%2E+Bennett%2C+and+Marti+A%2E+Hearst%2E+2022%2E+SummaC%3A+Re%2Dvisiting+NLI%2Dbased+models+for+inconsistency+detection+in+summarization%2E+Transactions+of+the+Association+for+Comp) 18. Deren Lei, Yaxi Li, Siyao Li, Mengya Hu, Rui Xu, Ken Archer, Mingyu Wang, Emily Ching, and Alex Deng. 2025. FactCG: Enhancing fact checkers with graph-based multi-hop data. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics:… [find](https://scholar.google.com/scholar?q=Deren+Lei%2C+Yaxi+Li%2C+Siyao+Li%2C+Mengya+Hu%2C+Rui+Xu%2C+Ken+Archer%2C+Mingyu+Wang%2C+Emily+Ching%2C+and+Alex+Deng%2E+2025%2E+FactCG%3A+Enhancing+fact+checkers+with+graph%2Dbased+multi%2Dhop+data%2E+In+Proceedings+of+the+2025+) 19. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics. 20. Nelson Liu, Tianyi Zhang, and Percy Liang. 2023a. [Evaluating verifiability in generative search engines](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7001–7025, Singapore. Association for Computational Linguistics. 21. Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023b. [G-eval: NLG evaluation using gpt-4 with better human alignment](https://md.research.elamur.ai/free/en/papers/liu-2023-g-eval-nlg-evaluation-gpt/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511–2522, Singapore. Association for Computational Li… 22. Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023. [Chatgpt as a factual inconsistency evaluator for text summarization](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/). arXiv preprint arXiv:2303.15621. 23. Chaitanya Malaviya, Subin Lee, Sihao Chen, Elizabeth Sieber, Mark Yatskar, and Dan Roth. 2024. [ExpertQA: Expert-curated questions and attributed answers](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies… 24. Yuxin Jiang, Chunkit Chan, Mingyang Chen, and Wei Wang. 2023. [Lion: Adversarial distillation of proprietary large language models](https://md.research.elamur.ai/free/en/papers/jiang-2023-lion-adversarial-distillation-proprietary-large/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3134–3154, Singapore. Association for Computational Linguistics. 25. Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Hosseini, Mark Johnson, and Mark Steedman. 2023. [Sources of hallucination by large language models on inference tasks](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2758–2774, Singapore. Association for Computational Li… 26. Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, and Greg Durrett. 2023. [WiCE: Real-world entailment for claims in Wikipedia](https://md.research.elamur.ai/free/en/papers/kamoi-2023-wice-real-world-entailment-claims/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7561–7583, Singapore. Association for Computational Linguistics. 27. Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, KaShun Shum, Randy Zhong, Juntong Song, and Tong Zhang. 2024. [RAGTruth: A hallucination corpus for developing trustworthy retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volu… 28. Association for Computational Linguistics: EMNLP 2024, pages 14199–14230, Miami, Florida, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Association+for+Computational+Linguistics%3A+EMNLP+2024%2C+pages+14199%E2%80%9314230%2C+Miami%2C+Florida%2C+USA%2E+Association+for+Computational+Linguistics%2E) 29. Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021. [Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics](https://md.research.elamur.ai/free/en/papers/pagnoni-2021-understanding-factuality-abstractive-summarization-frank/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Languag… 30. Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu, Jinho D. Choi, Jie Zhou, and Fei Liu. 2024. Identifying factual inconsistencies in summaries: Grounding LLM inference via task taxonomy. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 14626–14641, Miami, Florida, USA. Association… [find](https://scholar.google.com/scholar?q=Liyan+Xu%2C+Zhenlin+Su%2C+Mo+Yu%2C+Jin+Xu%2C+Jinho+D%2E+Choi%2C+Jie+Zhou%2C+and+Fei+Liu%2E+2024%2E+Identifying+factual+inconsistencies+in+summaries%3A+Grounding+LLM+inference+via+task+taxonomy%2E+In+Findings+of+the+Associa) 31. QwenTeam. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). Preprint, arXiv:2505.09388. 32. Deborah Yapp, Rick de Graaff, and Huub van den Bergh. 2023. Effects of reading strategy instruction in english as a second language on students’ academic reading comprehension. Language Teaching Research, 27(6):1456–1479. [find](https://scholar.google.com/scholar?q=Deborah+Yapp%2C+Rick+de+Graaff%2C+and+Huub+van+den+Bergh%2E+2023%2E+Effects+of+reading+strategy+instruction+in+english+as+a+second+language+on+students%E2%80%99+academic+reading+comprehension%2E+Language+Teaching+Resea) 33. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. [Direct preference optimization: Your language model is secretly a reward model](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/). Advances in neural information processing systems, 36:53728–53741. 34. Liyan Tang, Tanya Goyal, Alex Fabbri, Philippe Laban, Jiacheng Xu, Semih Yavuz, Wojciech Kryscinski, Justin Rousseau, and Greg Durrett. 2023. [Understanding factual errors in summarization: Errors, summarizers, datasets, error detectors](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/). In Proceedings of the 61st Annual Meeting of the Association fo… 35. Yuxuan Ye, Edwin Simpson, and Raul Santos Rodriguez. 2024. Using similarity to evaluate factual consistency in summaries. arXiv preprint arXiv:2409.15090. — [arXiv:2409.15090](https://arxiv.org/abs/2409.15090) [find](https://scholar.google.com/scholar?q=Yuxuan+Ye%2C+Edwin+Simpson%2C+and+Raul+Santos+Rodriguez%2E+2024%2E+Using+similarity+to+evaluate+factual+consistency+in+summaries%2E+arXiv+preprint+arXiv%3A2409%2E15090%2E+%E2%80%94+%5BarXiv%3A2409%2E15090%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F24) 36. Yuheng Zha, Yichi Yang, Ruichen Li, and Zhiting Hu. 2023. [AlignScore: Evaluating factual consistency with a unified alignment function](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11328–11348, Toronto, Canada. Association… 37. Liyan Tang, Philippe Laban, and Greg Durrett. 2024a. [MiniCheck: Efficient fact-checking of LLMs on grounding documents](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8818–8847, Miami, Florida, USA. Association for Computational Linguistics. 38. Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A. Smith. 2024. [How language model hallucinations can snowball](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/). In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pages 59670–59684. PMLR. 39. Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, and Kathleen McKeown. 2024b. TofuEval: Evaluating hallucinations of LLMs on topic-focused dialogue summarization. In Proceedings of the 2024… [find](https://scholar.google.com/scholar?q=Liyan+Tang%2C+Igor+Shalyminov%2C+Amy+Wong%2C+Jon+Burnsky%2C+Jake+Vincent%2C+Yu%E2%80%99an+Yang%2C+Siffi+Singh%2C+Song+Feng%2C+Hwanjun+Song%2C+Hang+Su%2C+Lijia+Sun%2C+Yi+Zhang%2C+Saab+Mansour%2C+and+Kathleen+McKeown%2E+2024b%2E+TofuEval%3A+E) 40. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675. [find](https://scholar.google.com/scholar?q=Tianyi+Zhang%2C+Varsha+Kishore%2C+Felix+Wu%2C+Kilian+Q+Weinberger%2C+and+Yoav+Artzi%2E+2019%2E+Bertscore%3A+Evaluating+text+generation+with+bert%2E+arXiv+preprint+arXiv%3A1904%2E09675%2E) 41. Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and answering questions to evaluate the factual consistency of summaries. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5008–5020, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alex+Wang%2C+Kyunghyun+Cho%2C+and+Mike+Lewis%2E+2020%2E+Asking+and+answering+questions+to+evaluate+the+factual+consistency+of+summaries%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Comput) 42. Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, and Preslav Nakov. 2024. Factcheck-bench: Fine-grained evaluation benchmark for automat… [find](https://scholar.google.com/scholar?q=Yuxia+Wang%2C+Revanth+Gangi+Reddy%2C+Zain+Muhammad+Mujahid%2C+Arnav+Arora%2C+Aleksandr+Rubashevskii%2C+Jiahui+Geng%2C+Osama+Mohammed+Afzal%2C+Liangming+Pan%2C+Nadav+Borenstein%2C+Aditya+Pillai%2C+Isabelle+Augenstein%2C+Iry) *Updated 2 November 2026*