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Arthur SATOUF

Université Paris-Saclay
Superviseur⋅e principal⋅e
Pablo Piantanida
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage par renforcement
Modèles génératifs
Recherche d'information
Traitement du langage naturel
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GitHub

Publications

Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval
Arthur SATOUF
Gabriel Ben-Zenou
Benjamin Piwowarski
Habiboulaye Amadou-Boubacar
Pablo Piantanida
Current sparse neural information retrieval (IR) methods, and to a lesser extent more traditional models such as BM25, do not take into acco… (voir plus)unt the document collection and the complex interplay between different term weights when representing a single document. In this paper, we show how the Rational Speech Acts (RSA), a linguistics framework used to minimize the number of features to be communicated when identifying an object in a set, can be adapted to the IR case -- and in particular to the high number of potential features (here, tokens). RSA dynamically modulates token-document interactions by considering the influence of other documents in the dataset, better contrasting document representations. Experiments show that incorporating RSA consistently improves multiple sparse retrieval models and achieves state-of-the-art performance on out-of-domain datasets from the BEIR benchmark. https://github.com/arthur-75/Rational-Retrieval-Acts
2025-07-12
International ACM SIGIR Conference on Research and Development in Information Retrieval (publié)
doi.org
arxiv.org