ETW uses predictive entropy as a proxy for token informativeness to improve selective unlearning in LLMs, achieving better forgetting with less utility loss than prior token-level methods.
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for
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APCD adaptively branches LLM decoding paths based on token entropy and contrasts divergent paths to improve factual accuracy while preserving efficiency.
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Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens
ETW uses predictive entropy as a proxy for token informativeness to improve selective unlearning in LLMs, achieving better forgetting with less utility loss than prior token-level methods.
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APCD: Adaptive Path-Contrastive Decoding for Reliable Large Language Model Generation
APCD adaptively branches LLM decoding paths based on token entropy and contrasts divergent paths to improve factual accuracy while preserving efficiency.