SetCSE：使用句子嵌入对比学习的集合运算
SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings
AI总结：
受集合论启发提出SetCSE信息检索框架，通过集合间对比学习目标增强句子嵌入模型对复杂语义的理解，并提供交集、差分等集合操作，显著提升模型判别能力，支持现有方法无法实现的复杂提示检索任务。
摘要：
受集合论启发，我们引入了SetCSE，一个创新的信息检索框架。SetCSE采用集合来表示复杂语义，并整合了定义良好的操作，用于在给定上下文下进行结构化信息查询。在此框架内，我们引入了集合间对比学习目标，以增强句子嵌入模型对给定语义的理解。此外，我们提出了一套操作，包括SetCSE交集、差分和操作系列，利用增强模型的句子嵌入来完成复杂的句子检索任务。贯穿全文，我们证明SetCSE遵循人类语言关于复合语义的表达习惯，显著提升了底层句子嵌入模型的判别能力，并支持众多涉及复杂且精细提示的信息检索任务，这是现有查询方法无法实现的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
