--- title: "Who cites R-Tuning: Instructing Large — 74 citing papers" description: "74 papers of the Elamur Research library cite «R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'», found in their…" canonical: "https://research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [R-Tuning: Instructing Large Language Models to…](https://md.research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/) / cited by # Who cites «R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'» 74 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'»](https://md.research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/) 1. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations* 2. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models arXiv:2401.01301 · Journal of Legal Analysis 16, no. 1 (2024): 64-93**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 437 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/) 3. [**A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models arXiv:2401.01313**S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, et al.*2024 · 249 citations*](https://md.research.elamur.ai/free/en/papers/tonmoy-2024-comprehensive-survey-hallucination-mitigation-techniques/) 4. **[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/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 207 citations* 5. **[Factuality of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2024-factuality-large-language-models-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, et al.*2024 · 77 citations* 6. **[ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://md.research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zihan Liu, Wei Ping, Rajarshi Roy, et al.*2024 · 58 citations* 7. **[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* 8. **[Knowledge Boundary of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/li-2025-knowledge-boundary-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Moxin Li, Yong Zhao, Wenxuan Zhang, et al.*2025 · 30 citations* 9. **[Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-stable-rag-mitigating-retrieval-permutation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qianchi Zhang, Hainan Zhang, Liang Pang, et al.*2026 · 27 citations* 10. **[Aligning LLMs with Individual Preferences via Interaction](https://md.research.elamur.ai/free/en/papers/wu-2025-aligning-llms-individual-preferences-interaction/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Shujin Wu, Yi R. Fung, Cheng Qian, et al.*2025 · 24 citations* 11. **[Conformity in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2025-conformity-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaochen Zhu, Caiqi Zhang, Tom Stafford, et al.*2025 · 18 citations* 12. **[Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?](https://md.research.elamur.ai/free/en/papers/mei-2026-reasoning-about-uncertainty-do-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiting Mei, Christina Zhang, Tenny Yin, et al.*2026 · 14 citations* 13. **[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* 14. **[BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning](https://md.research.elamur.ai/free/en/papers/nie-2025-bmike-53-investigating-cross-lingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ercong Nie, Bo Shao, Mingyang Wang, et al.*2025 · 12 citations* 15. **[MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs](https://md.research.elamur.ai/free/en/papers/liu-2025-metafaith-faithful-natural-language-uncertainty/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, et al.*2025 · 6 citations* 16. **[GREYC at CRF Filling 2026: Rewrite Before You Extract - Rewriting Clinical Notes for Automated CRF](https://md.research.elamur.ai/free/en/papers/melgarejo-2026-greyc-crf-filling-2026-rewrite/) [Workshop on Patient-Oriented Language Processing @ LREC](https://md.research.elamur.ai/free/en/venues/cl4health/)**Jesus Lovon-Melgarejo, Jérémie Pantin, Gaël Dias*2026* 17. **[How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study](https://md.research.elamur.ai/free/en/papers/yang-2026-how-do-llms-vlms-understand/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhen Yang, Ping Jian, Zhongbin Guo, et al.*2026* 18. **[LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-lovec-reinforcement-learning-better-verbalized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Caiqi Zhang, Xiaochen Zhu, Chengzu Li, et al.*2026* 19. **[MindFlayer at SemEval-2026 Task 8:DUALRAG:Answerability-Aware Generation for Multi-Turn RAG Conversations](https://md.research.elamur.ai/free/en/papers/tuli-2026-mindflayer-semeval-2026-task-8/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Jerin Romijah Tuli, Md. Sartaj Alam Pritom, Talukder Naemul Hasan Naem*2026* 55 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/) — R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'