为部分关键词丢失的检索场景设计了重要性感知冗余机制
Context-Aware Search and Retrieval Under Token Erasure
- 根据词频与语义重要性动态分配冗余信息
- 相似度差距向量收敛至多元高斯分布，可计算误差上界
- 适用于关键词易丢失的RAG系统，提升检索鲁棒性
本文研究了在查询表示部分丢失（{token} erasures）条件下远程文档检索的性能。采用基于词频的特征表示查询，依据特征重要性分配语义自适应冗余，通过加权TF-IDF进行检索。我们从信息论角度分析，发现相似度差距向量收敛于多元高斯分布，从而获得误差概率的显式近似和可计算上界。数值结果验证了理论分析，另在真实数据上的嵌入式检索评估表明，该重要性感知冗余原则同样适用于现代检索流程。总体结果表明，对语义关键特征施加更高冗余能显著提升检索可靠性。
原文摘要 · Abstract (English)
This paper introduces and analyzes a search and retrieval model for RAG-like systems under {token} erasures. We provide an information-theoretic analysis of remote document retrieval when query representations are only partially preserved. The query is represented using term-frequency-based features, and semantically adaptive redundancy is assigned according to feature importance. Retrieval is performed using TF-IDF-weighted similarity. We characterize the retrieval error probability by showing that the vector of similarity margins converges to a multivariate Gaussian distribution, yielding an explicit approximation and computable upper bounds. Numerical results support the analysis, while a separate data-driven evaluation using embedding-based retrieval on real-world data shows that the same importance-aware redundancy principles extend to modern retrieval pipelines. Overall, the results show that assigning higher redundancy to semantically important query features improves retrieval reliability.
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