Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models (2025.emnlp-main)
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Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, Gleb Kuzmin, Ivan Lazichny, Alexander Panchenko, Preslav Nakov, Timothy Baldwin, Maxim Panov, Artem Shelmanov
| Challenge: | Uncertainty quantification (UQ) is a promising approach for detecting hallucinations and low-quality outputs of Large Language Models (LLMs). |
| Approach: | They propose to learn conditional dependency between autoregressive LLM generation steps from attention-based features and a two-staged training procedure to incorporate recurrent features. |
| Outcome: | The proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches. |
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| Challenge: | Large language models (LLMs) produce hallucinations, which undermine user trust and reliability. |
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Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee, Radu Marinescu, Katya Mirylenka, Michael Glass, Xiao Shou
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| Challenge: | Large language models have a tendency to make confidently wrong predictions, highlighting the need for uncertainty quantification (UQ) . previous studies focused on aleatoric uncertainty, but the full spectrum of uncertainties, including epistemic, remains inadequately explored. |
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| Challenge: | Existing methods conflate fluency with correctness or require substantial computational overhead. |
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ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)
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Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu
| Challenge: | Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs. |
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