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Such approaches can take advantage of the proven efficiency of inverted indexes and inherit desirable IR priors such as explicit lexical matching or some degree of interpretability. In this work, we thoroughly develop the framework of sparse representation learning in IR, which unifies term weighting and expansion in a supervised setting. We then build on SPLADE\u2014a sparse expansion-based retriever\u2014and show to which extent it is able to benefit from the same training improvements as dense bi-encoders by studying the effect of distillation, hard negative mining, as well as the Pre-trained Language Model\u2019s initialization on its<jats:italic>effectiveness<\/jats:italic>, leading to state-of-the-art results in both in- and out-of-domain evaluation settings (SPLADE++). We furthermore propose<jats:italic>efficiency<\/jats:italic>improvements, allowing us to reach latency requirements on par with traditional keyword-based approaches (Efficient-SPLADE).<\/jats:p>","DOI":"10.1145\/3634912","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T11:58:14Z","timestamp":1701518294000},"page":"1-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Towards Effective and Efficient Sparse Neural Information Retrieval"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6363-9553","authenticated-orcid":false,"given":"Thibault","family":"Formal","sequence":"first","affiliation":[{"name":"Naver Labs Europe, Meylan, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7754-6656","authenticated-orcid":false,"given":"Carlos","family":"Lassance","sequence":"additional","affiliation":[{"name":"Naver Labs Europe, Meylan, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6792-3262","authenticated-orcid":false,"given":"Benjamin","family":"Piwowarski","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, CNRS, ISIR, Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2367-8837","authenticated-orcid":false,"given":"St\u00e9phane","family":"Clinchant","sequence":"additional","affiliation":[{"name":"Naver Labs Europe, Meylan, France"}]}],"member":"320","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E. 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