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        <title>Improve Dense Passage Retrieval with Entailment Tuning</title>
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            <namePart type="given">Mohit</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Improve Dense Passage Retrieval with Entailment Tuning
%A Dai, Lu
%A Liu, Hao
%A Xiong, Hui
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F dai-etal-2024-improve
%X 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.
%U https://aclanthology.org/2024.emnlp-main.636
%P 11375-11387

Markdown (Informal)

[Improve Dense Passage Retrieval with Entailment Tuning](https://aclanthology.org/2024.emnlp-main.636) (Dai et al., EMNLP 2024)

• Improve Dense Passage Retrieval with Entailment Tuning (Dai et al., EMNLP 2024)

ACL

• Lu Dai, Hao Liu, and Hui Xiong. 2024. Improve Dense Passage Retrieval with Entailment Tuning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11375–11387, Miami, Florida, USA. Association for Computational Linguistics.