Enhancing Sentence Retrieval through Set-based Contrastive Learning of Sentence Embeddings

SetCSE, a novel information retrieval framework, employs sets to represent complex semantics and incorporates well-defined operations for structured querying. The proposed inter-set contrastive learning objective significantly enhances the discriminatory capability of underlying sentence embedding models, enabling numerous information retrieval tasks involving intricate prompts.