---
title: "Who cites Factually Consistent — 46 citing papers"
description: "46 papers of the Elamur Research library cite «Factually Consistent Summarization via Reinforcement Learning with Textual Entailment…"
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---
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# Who cites «Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback»

46 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback»](https://md.research.elamur.ai/free/en/papers/roit-2023-factually-consistent-summarization-reinforcement-learning/)

1. [**Chain-of-Verification Reduces Hallucination in Large Language Models arXiv:2309.11495**Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/dhuliawala-2023-chain-verification-reduces-hallucination-large/)
2. [**On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes arXiv:2306.13649**Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al.*2023 · 299 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-policy-distillation-language-models-learning/)
3. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
4. [**A Survey of Large Language Models in Medicine: Progress, Application, and Challenge arXiv:2311.05112**Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/)
5. **[Unfamiliar Finetuning Examples Control How Language Models Hallucinate](https://md.research.elamur.ai/free/en/papers/kang-2025-unfamiliar-finetuning-examples-control-how/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Katie Kang, Eric Wallace, Claire Tomlin, et al.*2025 · 47 citations*
6. [**The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input arXiv:2501.03200**Alon Jacovi, Andrew Wang, Chris Alberti, et al.*2025 · 32 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2025-facts-grounding-leaderboard-benchmarking-llms/)

40 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/roit-2023-factually-consistent-summarization-reinforcement-learning/) — Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback