@inproceedings{ma-etal-2026-corect,
title = "{C}o{R}ect: Context-Aware Logit Contrast for Hidden State Rectification to Resolve Knowledge Conflicts",
author = "Ma, Xuhua and
Zhang, Richong and
Nie, Zhijie",
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.539/",
pages = "12414--12432",
ISBN = "979-8-89176-459-0",
abstract = "Retrieval-Augmented Generation (RAG) can fail under knowledge conflicts, where a model{'}s parametric prior competes with retrieved evidence and leads to unfaithful outputs. Existing mitigation methods typically operate on the final decoding distribution or rely on weight editing with access to ground-truth targets. Through layer-wise analysis, we identify a **parametric suppression** pattern: in many conflict cases, evidence-supported answers become salient in intermediate representations but are weakened by later layer outputs. To address this, we propose CoRect (**Co**ntext-Aware Logit Contrast for Hidden State **Rect**ification), a training-free inference-time method. By contrasting contextualized and context-free forward passes, CoRect first selects an evidence-supported token direction without using gold labels. It then rectifies hidden states in layers whose outputs suppress this direction. Across Question Answering (QA) and summarization benchmarks, CoRect improves average faithfulness and reduces hallucinations compared with strong decoding baselines."
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<abstract>Retrieval-Augmented Generation (RAG) can fail under knowledge conflicts, where a model’s parametric prior competes with retrieved evidence and leads to unfaithful outputs. Existing mitigation methods typically operate on the final decoding distribution or rely on weight editing with access to ground-truth targets. Through layer-wise analysis, we identify a **parametric suppression** pattern: in many conflict cases, evidence-supported answers become salient in intermediate representations but are weakened by later layer outputs. To address this, we propose CoRect (**Co**ntext-Aware Logit Contrast for Hidden State **Rect**ification), a training-free inference-time method. By contrasting contextualized and context-free forward passes, CoRect first selects an evidence-supported token direction without using gold labels. It then rectifies hidden states in layers whose outputs suppress this direction. Across Question Answering (QA) and summarization benchmarks, CoRect improves average faithfulness and reduces hallucinations compared with strong decoding baselines.</abstract>
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%0 Conference Proceedings
%T CoRect: Context-Aware Logit Contrast for Hidden State Rectification to Resolve Knowledge Conflicts
%A Ma, Xuhua
%A Zhang, Richong
%A Nie, Zhijie
%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 ma-etal-2026-corect
%X Retrieval-Augmented Generation (RAG) can fail under knowledge conflicts, where a model’s parametric prior competes with retrieved evidence and leads to unfaithful outputs. Existing mitigation methods typically operate on the final decoding distribution or rely on weight editing with access to ground-truth targets. Through layer-wise analysis, we identify a **parametric suppression** pattern: in many conflict cases, evidence-supported answers become salient in intermediate representations but are weakened by later layer outputs. To address this, we propose CoRect (**Co**ntext-Aware Logit Contrast for Hidden State **Rect**ification), a training-free inference-time method. By contrasting contextualized and context-free forward passes, CoRect first selects an evidence-supported token direction without using gold labels. It then rectifies hidden states in layers whose outputs suppress this direction. Across Question Answering (QA) and summarization benchmarks, CoRect improves average faithfulness and reduces hallucinations compared with strong decoding baselines.
%U https://aclanthology.org/2026.emnlp-main.539/
%P 12414-12432
Markdown (Informal)
[CoRect: Context-Aware Logit Contrast for Hidden State Rectification to Resolve Knowledge Conflicts](https://aclanthology.org/2026.emnlp-main.539/) (Ma et al., EMNLP 2026)
ACL