@inproceedings{lin-etal-2026-bargaining,
title = "Bargaining with Internal Evidence: Receiver-Constrained Decoding for Factual Reliability in Large Language Models",
author = "Lin, Chenhao and
Zhang, Mouyi and
Chen, Peng and
Zheng, Xin and
Hong, Yuejia and
Gao, Nan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1981/",
pages = "45068--45081",
ISBN = "979-8-89176-459-0",
abstract = "Large language models can produce fluent yet factually incorrect responses, limiting their reliability in knowledge-intensive applications. Recent decoding methods seek to mitigate such hallucinations by contrasting or amplifying signals from intermediate layers. However, not every layerwise discrepancy provides a reliable direction for factual decoding. We propose a unified game-theoretic decoding framework for large language models: intermediate-layer signals act as competing evidence, while the final layer retains decision authority under explicit constraints. Rather than applying a fixed layerwise contrast, our framework treats changes in intermediate predictions as evidence of the language model{'}s evolving internal judgment, and uses this evidence to guide decoding under final-layer constraints. Experiments with three backbone language models on multiple truthfulness and hallucination benchmarks demonstrate the effectiveness of our framework in improving factual reliability."
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%0 Conference Proceedings
%T Bargaining with Internal Evidence: Receiver-Constrained Decoding for Factual Reliability in Large Language Models
%A Lin, Chenhao
%A Zhang, Mouyi
%A Chen, Peng
%A Zheng, Xin
%A Hong, Yuejia
%A Gao, Nan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lin-etal-2026-bargaining
%X Large language models can produce fluent yet factually incorrect responses, limiting their reliability in knowledge-intensive applications. Recent decoding methods seek to mitigate such hallucinations by contrasting or amplifying signals from intermediate layers. However, not every layerwise discrepancy provides a reliable direction for factual decoding. We propose a unified game-theoretic decoding framework for large language models: intermediate-layer signals act as competing evidence, while the final layer retains decision authority under explicit constraints. Rather than applying a fixed layerwise contrast, our framework treats changes in intermediate predictions as evidence of the language model’s evolving internal judgment, and uses this evidence to guide decoding under final-layer constraints. Experiments with three backbone language models on multiple truthfulness and hallucination benchmarks demonstrate the effectiveness of our framework in improving factual reliability.
%U https://aclanthology.org/2026.emnlp-main.1981/
%P 45068-45081
Markdown (Informal)
[Bargaining with Internal Evidence: Receiver-Constrained Decoding for Factual Reliability in Large Language Models](https://aclanthology.org/2026.emnlp-main.1981/) (Lin et al., EMNLP 2026)
ACL