评估RAG在干净、误导和混合检索下的可靠性
Evaluating RAG Reliability under Clean, Misleading, and Mixed Retrieval
AI总结：
提出评估协议，通过参数覆盖和置信度指标，系统测试RAG系统在干净、有毒和混合证据下处理参数知识与检索证据冲突的鲁棒性。
摘要：
检索增强生成（RAG）通过将答案基于检索到的证据，被广泛用于提高大型语言模型（LLMs）的事实可靠性。然而，在信息丰富的误导环境中，检索到的内容可能包含看似合理但不正确的信息，引发了对基于RAG的信息访问系统可靠性的担忧。在这项工作中，我们提出了一种评估协议，以系统地测试RAG系统如何处理参数知识与从具有不同数量误导信息的上下文中检索到的证据之间的冲突。我们针对模型在无检索时也能正确回答的事实性问题，使用干净、有毒和混合证据来测试系统。所提出的分析框架结合了参数覆盖和置信度指标，以评估误导信息何时以及如何影响LLMs的生成过程。本研究旨在为信息混乱场景下RAG系统的鲁棒性提供见解。
英文摘要：
Retrieval-Augmented Generation (RAG) is widely used to improve the factual reliability of large language models (LLMs) by grounding answers in retrieved evidence. In misinformation-rich environments, however, retrieved content may include plausible but incorrect information, raising concerns about the reliability of RAG-based information access systems. In this work, we propose an evaluation protocol to systematically test how the RAG system handles conflicts between parametric knowledge and evidence retrieved from context with varying amounts of misleading information. We target correct answers to factoid questions that the model responds to correctly, even when there is no retrieval, and use this to test the system with clean, poisoned, and mixed evidence. The proposed analytical framework combines parametric override and confidence metrics to assess when and how misleading information affects the generation process of LLMs. This study aims to provide insights into the robustness of RAG systems in information disorder scenarios.
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