  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.
