---
title: "Who cites Unfamiliar Finetuning Examples — 40 citing papers"
description: "40 papers of the Elamur Research library cite «Unfamiliar Finetuning Examples Control How Language Models Hallucinate», found in their…"
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---
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# Who cites «Unfamiliar Finetuning Examples Control How Language Models Hallucinate»

40 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · [← back to «Unfamiliar Finetuning Examples Control How Language Models Hallucinate»](https://md.research.elamur.ai/free/en/papers/kang-2025-unfamiliar-finetuning-examples-control-how/)

1. **[Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://md.research.elamur.ai/free/en/papers/qu-2024-recursive-introspection-teaching-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.*2024 · 75 citations*
2. **[Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://md.research.elamur.ai/free/en/papers/yona-2024-can-large-language-models-faithfully/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gal Yona, Roee Aharoni, Mor Geva*2024 · 40 citations*
3. **[LoGU: Long-form Generation with Uncertainty Expressions](https://md.research.elamur.ai/free/en/papers/yang-2025-logu-long-form-generation-uncertainty/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruihan Yang, Caiqi Zhang, Zhisong Zhang, et al.*2025 · 13 citations*
4. **[Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering](https://md.research.elamur.ai/free/en/papers/si-2025-aligning-large-language-models-follow/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuzheng Si, Haozhe Zhao, Gang Chen, et al.*2025 · 11 citations*
5. **[KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality](https://md.research.elamur.ai/free/en/papers/ren-2026-knowrl-exploring-knowledgeable-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Baochang Ren, Shuofei Qiao, Ningyu Zhang, et al.*2026 · 5 citations*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/kang-2025-unfamiliar-finetuning-examples-control-how/) — Unfamiliar Finetuning Examples Control How Language Models Hallucinate