提出新方法量化并提升RAG模型对隐藏干扰特征的鲁棒性
Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data
- 构建SURE框架，实证评估LLM在检索增强下的敏感度
- 发现语义无关特征普遍引发错误，影响超过60%的测试样本
- 适合关注真实场景下RAG系统稳定性的研究者与工程师
鲁棒性已成为RAG系统在实际应用中部署的关键属性。现有研究主要关注显式噪声（如文档语义）的鲁棒性，却忽略了隐式噪声（即虚假特征）。此外，以往关于大语言模型中虚假特征的研究局限于特定类型（如格式）和狭窄场景（如上下文学习）。本文首次识别并研究了RAG范式中的虚假特征问题，该问题源于大模型对语义无关特征的敏感性。我们提出了一个名为SURE的新框架，用于实证量化检索增强语言模型（RALMs）对虚假特征的鲁棒性。该框架不仅提供全面的分类体系与评估指标，其数据合成流程还支持基于训练的鲁棒性提升策略。进一步分析表明，虚假特征是RAG领域广泛存在且严峻的挑战。代码已公开于https://github.com/maybenotime/RAG-SpuriousFeatures。
原文摘要 · Abstract (English)
Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustness to explicit noise (e.g., document semantics) but overlooks implicit noise (spurious features). Moreover, previous studies on spurious features in LLMs are limited to specific types (e.g., formats) and narrow scenarios (e.g., ICL). In this work, we identify and study spurious features in the RAG paradigm, a robustness issue caused by the sensitivity of LLMs to semantic-agnostic features. We then propose a novel framework, SURE, to empirically quantify the robustness of RALMs against spurious features. Beyond providing a comprehensive taxonomy and metrics for evaluation, the framework's data synthesis pipeline facilitates training-based strategies to improve robustness. Further analysis suggests that spurious features are a widespread and challenging problem in the field of RAG. Our code is available at https://github.com/maybenotime/RAG-SpuriousFeatures .
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