Papers with Density-based methods
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models (2025.naacl-long)
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Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov
| Challenge: | Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs). |
| Approach: | They propose to use a well-established method for text generation to extract token embeddings from multiple layers of LLMs and compute MD scores for each token. |
| Outcome: | The proposed method improves on existing methods and provides accurate and computationally efficient uncertainty scores for sequence-level selective generation and claim-level fact-checking tasks. |