@inproceedings{zhao-etal-2026-tokens,
title = "What Tokens Truly Matter? The Logit Conflation Problem in {LLM} Sampling",
author = "Zhao, Pinlong and
Tang, Huijun and
Jiao, Pengfei and
Li, Mengyang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1841/",
pages = "36943--36961",
ISBN = "979-8-89176-395-1",
abstract = "Sampling methods for large language models select candidate tokens based on logit statistics, implicitly assuming that high logits indicate desirable outputs. We identify the Logit Conflation Problem, where a token{'}s logit aggregates prompt-independent factors, including linguistic fluency and parametric associations, with prompt-relevance. However, only prompt-relevance determines instruction-following quality. We propose SEAL-Sampling (Signal Extraction for Active ReLevance) to isolate this component through attention-weighted attribution. Our framework defines prompt-relevance as the causal effect of prompt content on token logits and establishes attention patterns as an efficient proxy. Experiments on LLaMA-3 demonstrate significant improvements over top-n{\ensuremath{\sigma}}, with gains of 1.8{\%} on AlpacaEval 2.0 and 2.2{\%} on IFEval. Furthermore, attribution scores correlate weakly with raw logits, confirming the extraction of an orthogonal signal. The method is training-free and introduces minimal latency, adding less than 12ms overhead per token."
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<abstract>Sampling methods for large language models select candidate tokens based on logit statistics, implicitly assuming that high logits indicate desirable outputs. We identify the Logit Conflation Problem, where a token’s logit aggregates prompt-independent factors, including linguistic fluency and parametric associations, with prompt-relevance. However, only prompt-relevance determines instruction-following quality. We propose SEAL-Sampling (Signal Extraction for Active ReLevance) to isolate this component through attention-weighted attribution. Our framework defines prompt-relevance as the causal effect of prompt content on token logits and establishes attention patterns as an efficient proxy. Experiments on LLaMA-3 demonstrate significant improvements over top-n\ensuremathσ, with gains of 1.8% on AlpacaEval 2.0 and 2.2% on IFEval. Furthermore, attribution scores correlate weakly with raw logits, confirming the extraction of an orthogonal signal. The method is training-free and introduces minimal latency, adding less than 12ms overhead per token.</abstract>
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%0 Conference Proceedings
%T What Tokens Truly Matter? The Logit Conflation Problem in LLM Sampling
%A Zhao, Pinlong
%A Tang, Huijun
%A Jiao, Pengfei
%A Li, Mengyang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhao-etal-2026-tokens
%X Sampling methods for large language models select candidate tokens based on logit statistics, implicitly assuming that high logits indicate desirable outputs. We identify the Logit Conflation Problem, where a token’s logit aggregates prompt-independent factors, including linguistic fluency and parametric associations, with prompt-relevance. However, only prompt-relevance determines instruction-following quality. We propose SEAL-Sampling (Signal Extraction for Active ReLevance) to isolate this component through attention-weighted attribution. Our framework defines prompt-relevance as the causal effect of prompt content on token logits and establishes attention patterns as an efficient proxy. Experiments on LLaMA-3 demonstrate significant improvements over top-n\ensuremathσ, with gains of 1.8% on AlpacaEval 2.0 and 2.2% on IFEval. Furthermore, attribution scores correlate weakly with raw logits, confirming the extraction of an orthogonal signal. The method is training-free and introduces minimal latency, adding less than 12ms overhead per token.
%U https://aclanthology.org/2026.findings-acl.1841/
%P 36943-36961
Markdown (Informal)
[What Tokens Truly Matter? The Logit Conflation Problem in LLM Sampling](https://aclanthology.org/2026.findings-acl.1841/) (Zhao et al., Findings 2026)
ACL