🔗 Who cites «Out-of-Distribution Detection and Selective Generation for Conditional Language Models»

68 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · ← back to «Out-of-Distribution Detection and Selective Generation for Conditional Language Models»

1. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
2. INSIDE: LLMs' Internal States Retain the Power of Hallucination DetectionarXiv:2402.03744Chao Chen, Kai Liu, Ze Chen, et al.2024 · 108 citations
3. A Survey of Confidence Estimation and Calibration in Large Language ModelsarXiv:2311.08298Jiahui Geng, Fengyu Cai, Yuxia Wang, et al.2023 · 97 citations
4. Fact-Checking the Output of Large Language Models via Token-Level Uncertainty QuantificationFindings of the Association for Computational LinguisticsEkaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, et al.2024 · 96 citations
5. Selectively Answering Ambiguous QuestionsConference on Empirical Methods in Natural Language Processing (EMNLP)Jeremy Cole, Michael Zhang, Daniel Gillick, et al.2023 · 84 citations
6. Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back HomeAnnual Meeting of the Association for Computational Linguistics (ACL)Viktor Moskvoretskii, Maria Marina, Mikhail Salnikov, et al.2025 · 21 citations
7. ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhenliang Zhang, Xinyu Hu, Huixuan Zhang, et al.2025 · 14 citations
8. Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, et al.2025 · 13 citations
9. Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data FilteringAnnual Meeting of the Association for Computational Linguistics (ACL)Shuzheng Si, Haozhe Zhao, Gang Chen, et al.2025 · 11 citations
10. Is That Your Final Answer? Test-Time Scaling Improves Selective Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)William Jurayj, Jeffrey Cheng, Benjamin Van Durme2025 · 11 citations
11. Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, et al.2025 · 10 citations
12. MUR: Momentum Uncertainty guided Reasoning for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hang Yan, Fangzhi Xu, Rongman Xu, et al.2026 · 8 citations
13. Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?International Joint Conference on Natural Language Processing (IJCNLP)Kai Yan, Yufei Xu, Zhengyin Du, et al.2025 · 5 citations
14. Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and OpportunitiesAnnual Meeting of the Association for Computational Linguistics (ACL)Changdae Oh, Seongheon Park, To Eun Kim, et al.2026 · 4 citations
15. Constructing a Dataset for Hallucination Detection in Japanese Summarization with Fine-grained Faithfulness LabelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Hikari Tanaka, Atsushi Keyaki, Mamoru Komachi2026
16. LatentRefusal: Latent-Signal Refusal for Unanswerable Text-to-SQL QueriesFindings of the Association for Computational LinguisticsXuancheng Ren, Shijing Hu, Zhihui Lu, et al.2026
17. Learning Uncertainty from Sequential Internal Dispersion in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ponhvoan Srey, Xiaobao Wu, Cong-Duy T Nguyen, et al.2026
18. RFS-Guard: Detecting Reasoning Hallucinations via Cross-Phase Routing Focus in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zihang Liu, Fang Zhouhua, Hui Liu, et al.2026
19. TPA: Next Token Probability Attribution for Detecting Hallucinations in RAGAnnual Meeting of the Association for Computational Linguistics (ACL)Pengqian Lu, Jie Lu, Anjin Liu, et al.2026
20. VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-EvaluationFindings of the Association for Computational LinguisticsSeongheon Park, Changdae Oh, Hyeong Kyu Choi, et al.2026

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

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/ren-2022-out-distribution-detection-selective-generation/cited-by/