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Résumé : 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.