🔗 Who cites «Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models»

12 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 «Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models»

1. 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
2. ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu, et al.2026 · 1 citation
3. Evolutionary Search for Automated Design of Uncertainty Quantification MethodsAnnual Meeting of the Association for Computational Linguistics (ACL)Mikhail Seleznyov, Daniil Korbut, Viktor Moskvoretskii, et al.2026
4. LAFaCT: Attribution-based Localization and Focused Sequential Analysis of Fact-Critical Tokens for Hallucination DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Xin Wang, Jiahao Li, Licheng Zhang, et al.2026
5. 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

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

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