mlsanityData to 7 Oct 2026

SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings

arXiv 2404.17606 ↗First posted 25 Apr 2024cs.IR cs.CL cs.LGcited by 2

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Abstract (arXiv)

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 for complex sentence retrieval tasks. Throughout this paper, we demonstrate that SetCSE adheres to the conventions of human language expressions regarding compounded semantics, provides a significant enhancement in the discriminatory capability of underlying sentence embedding models, and enables numerous information retrieval tasks involving convoluted and intricate prompts which cannot be achieved using existing querying methods.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 41 of its 106 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

41 of 106 references.

References · 41

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29 more
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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Set-Theoretic Compositionality of Sentence Embeddingscited by 0
  2. 2025Optimizing Conversational Product Recommendation via Reinforcement Learningcited by 0