Improve Dense Passage Retrieval with Entailment Tuning

• URL: http://arxiv.org/abs/2410.15801v1
• Date: Mon, 21 Oct 2024 09:18:30 GMT
• Title: Improve Dense Passage Retrieval with Entailment Tuning
• Authors: Lu Dai, Hao Liu, Hui Xiong,
• Abstract summary: Key to a retrieval system is to calculate relevance scores to query and passage pairs. We observed that a major class of relevance aligns with the concept of entailment in NLI tasks. We design a method called entailment tuning to improve the embedding of dense retrievers.
• Score: 22.39221206192245
• License:
• Abstract: Retrieval module can be plugged into many downstream NLP tasks to improve their performance, such as open-domain question answering and retrieval-augmented generation. The key to a retrieval system is to calculate relevance scores to query and passage pairs. However, the definition of relevance is often ambiguous. We observed that a major class of relevance aligns with the concept of entailment in NLI tasks. Based on this observation, we designed a method called entailment tuning to improve the embedding of dense retrievers. Specifically, we unify the form of retrieval data and NLI data using existence claim as a bridge. Then, we train retrievers to predict the claims entailed in a passage with a variant task of masked prediction. Our method can be efficiently plugged into current dense retrieval methods, and experiments show the effectiveness of our method.

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