Papers with token-level algorithm

1 papers
CoCoA: Confidence- and Context-Aware Adaptive Decoding for Resolving Knowledge Conflicts in Large Language Models (2025.emnlp-main)

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Challenge: Existing contrastive decoding methods that handle conflict lack adaptability and can degrade performance in low conflict settings.
Approach: They propose a token-level algorithm for principled conflict resolution and enhanced faithfulness that resolves conflict by utilizing confidence-aware measures and the generalized divergence between parametric and contextual distributions.
Outcome: The proposed algorithm achieves 9.2 points on average in QA, summarization, and long-form question answering (LFQA) benchmarks and improves factuality by 2.5 points on the key benchmarks.

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