SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings

arXiv:2404.17606v1 Announce Type: cross
Abstract: Taking inspiration from Set Theory, we introduce SetCSE, an innovative information retrieval framework. SetCSE employs sets to represent complex semantics and incorporates well-defined operations for structured information querying under the provided context. Within this framework, we introduce an inter-set contrastive learning objective to enhance comprehension of sentence embedding models concerning the given semantics. Furthermore, we present a suite of operations, including SetCSE intersection, difference, and operation series, that leverage sentence embeddings of the enhanced model …

abstract arxiv context cs.ir cs.lg embeddings framework information inspiration operations retrieval semantics set theory type