Problem
Research questions and friction points this paper is trying to address.
negative constraints
retrieval-augmented language models
dense retrievers
E-SENS: Exclusion-Sensitive Penalization for Negative-Constraint Retrieval
🤖 AI Summary
为解决检索增强语言模型在负约束检索中忽略用户排除概念的问题，提出E-SENS方法，通过减去陷阱查询相似度来优化检索结果。
📝 Abstract
Retrieval-augmented language models can fail to respect negative constraints when the retriever supplies evidence about concepts the user explicitly excluded. Beyond explicit negation, queries may ask for answers that include one concept while excluding another, or for entities that belong to a category but differ from a closely related instance. Because the excluded concept still appears in the query text, dense retrievers may assign high similarity to documents about that concept even when the user asks to avoid it. We introduce E-SENS, a training-free reranking method for negation-sensitive retrieval. E-SENS extracts a compact trap query for the excluded side and subtracts trap-query similarity from the original-query retrieval score. On ExcluIR, E-SENS shows a clear recall-violation trade-off across four embedding models and reduces trap retrieval at recall-preserving settings.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Exclusion-Sensitive
Penalization
Negative-Constraint Retrieval
Trap Query
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