Papers by Huijun Tang

1 papers
What Tokens Truly Matter? The Logit Conflation Problem in LLM Sampling (2026.findings-acl)

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Challenge: Existing methods for large language models filter tokens based on logit magnitudes or derived statistics, under the implicit assumption that high-logit tokens are desirable.
Approach: They propose to isolate the Logit Conflation Problem by using attention-weighted attribution to extract prompt-relevance from token logits.
Outcome: The proposed method improves on LLaMA-3 and is training-free and low latency.

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