让检索模型理解逻辑关系，提升复杂查询的准确性和一致性。
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval
- 引入逻辑约束的对比学习，用t-范数建模集合间的包含与互斥关系。
- 在维基百科实体检索任务中，召回率和逻辑一致性均显著提升。
- 特别适合需要精确逻辑推理的问答与信息检索场景。
尽管双编码器和双塔密集检索器已取得显著进展，但在包含逻辑连接词的查询上仍表现不佳，这类查询虽常被忽视却在下游应用中至关重要。现有密集检索器无法充分尊重查询中隐含的逻辑约束，导致结果不一致。为此，本文提出LogiCoL，一种基于逻辑信息的对比学习目标，用于改进密集检索器。LogiCoL基于批次内监督对比学习，通过t-范数表达的两组软约束，学习检索器识别查询结果之间的子集关系和互斥关系。我们在实体检索任务上评估了LogiCoL的有效性，要求模型从维基百科中检索满足查询隐含逻辑约束的一组实体。实验表明，使用LogiCoL训练的模型在检索性能和结果逻辑一致性方面均有提升。我们还提供了深入分析，揭示了逻辑连接词查询对密集检索器构成挑战的原因，以及LogiCoL为何最有效。
原文摘要 · Abstract (English)
While significant progress has been made with dual- and bi-encoder dense retrievers, they often struggle on queries with logical connectives, a use case that is often overlooked yet important in downstream applications. Current dense retrievers struggle with such queries, such that the retrieved results do not respect the logical constraints implied in the queries. To address this challenge, we introduce LogiCoL, a logically-informed contrastive learning objective for dense retrievers. LogiCoL builds upon in-batch supervised contrastive learning, and learns dense retrievers to respect the subset and mutually-exclusive set relation between query results via two sets of soft constraints expressed via t-norm in the learning objective. We evaluate the effectiveness of LogiCoL on the task of entity retrieval, where the model is expected to retrieve a set of entities in Wikipedia that satisfy the implicit logical constraints in the query. We show that models trained with LogiCoL yield improvement both in terms of retrieval performance and logical consistency in the results. We provide detailed analysis and insights to uncover why queries with logical connectives are challenging for dense retrievers and why LogiCoL is most effective.
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