arXiv 2510.124602026-04-15cs.CL

超越黑盒干预：用于忠实检索增强生成的潜在探测

Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation

School of Informatics, Xiamen University（厦门大学信息学院）；The Hong Kong Polytechnic University（香港理工大学）；University of Chinese Academy of Sciences（中国科学院大学）；Alibaba Group（阿里巴巴集团）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室（厦门大学），中华人民共和国文化和旅游部，中国）

Linfeng Gao, Qinggang Zhang, Baolong Bi, Bo Zeng, Zheng Yuan, Zerui Chen, Zhimin Wei, Shenghua Liu, Linlong Xu, Longyue Wang, Weihua Luo, Jinsong Su

备注：: ACL 2026 Findings; Code is available at https://github.com/LinfengGao/ProbeRAG

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出ProbeRAG框架，通过潜在冲突探测和注意力调节提升检索增强生成的忠实度，解决传统方法在评估知识冲突时的不足。

摘要：

检索增强生成（RAG）系统常无法维持上下文忠实性，生成与提供上下文冲突或未能充分利用证据的响应。现有方法通过外部干预如专门提示、解码校准或偏好优化改进忠实性，但这些方法将LLM视为黑盒，缺乏可靠机制评估知识冲突发生的时间和原因。本文超越黑盒干预，分析模型内部推理过程，发现冲突和一致的知识状态在模型潜在空间中线性可分离，上下文噪声系统性增加这些表示的熵。基于此，提出ProbeRAG框架，包含三个阶段：（i）细粒度知识修剪过滤无关上下文，（ii）潜在冲突探测识别模型潜在空间中的硬冲突，（iii）冲突感知注意力调节注意力头以实现忠实上下文整合。大量实验表明，ProbeRAG显著提升准确性和上下文忠实性。相关资源可在https://github.com/LinfengGao/ProbeRAG获取。

英文摘要：

Retrieval-Augmented Generation (RAG) systems often fail to maintain contextual faithfulness, generating responses that conflict with the provided context or fail to fully leverage the provided evidence. Existing methods attempt to improve faithfulness through external interventions, such as specialized prompting, decoding-based calibration, or preference optimization. However, since these approaches treat the LLM as a black box, they lack a reliable mechanism to assess when and why knowledge conflicts occur. Consequently, they tend to be brittle, data-intensive, and agnostic to the model's internal reasoning process. In this paper, we move beyond black-box interventions to analyze the model's internal reasoning process. We discover that conflicting and aligned knowledge states are linearly separable in the model's latent space, and contextual noise systematically increases the entropy of these representations. Based on these findings, we propose ProbeRAG, a novel framework for faithful RAG that operates in three stages: (i) fine-grained knowledge pruning to filter irrelevant context, (ii) latent conflict probing to identify hard conflicts in the model's latent space, and (iii) conflict-aware attention to modulate attention heads toward faithful context integration. Extensive experiments demonstrate that ProbeRAG substantially improves both accuracy and contextual faithfulness. The related resources are available at https://github.com/LinfengGao/ProbeRAG.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。