无需微调，用结构化语义索引高效检索对话数据
HEISIR: Hierarchical Expansion of Inverted Semantic Indexing for Training-free Retrieval of Conversational Data using LLMs
- 将对话转为主谓宾补四元组构建语义索引
- 在多种模型上超越微调模型的检索效果
- 适合需要快速部署的对话系统开发者
对话式AI服务的增长带来了对对话数据高效检索的需求。现有方法常难以捕捉语义意图，或需大量标注与微调。本文提出HEISIR（基于层级反向语义索引的检索框架），通过优化数据摄入流程，在无需资源密集型标注或模型适配的情况下提升对话数据的语义理解能力。该框架包含两步：(1) 层级三元组构建，(2) 补充增强，生成由主语-谓语-宾语-补语（SVOA）组成的语义索引。这种结构化表示能有效提取对话内容的深层语义信息。实验表明，HEISIR在不同嵌入类型和语言模型下均优于微调模型，同时具备低延迟特性。此外，该方法还支持对话数据中的意图与主题分析，为对话系统提供多功能解决方案。
原文摘要 · Abstract (English)
The growth of conversational AI services has increased demand for effective information retrieval from dialogue data. However, existing methods often face challenges in capturing semantic intent or require extensive labeling and fine-tuning. This paper introduces HEISIR (Hierarchical Expansion of Inverted Semantic Indexing for Retrieval), a novel framework that enhances semantic understanding in conversational data retrieval through optimized data ingestion, eliminating the need for resource-intensive labeling or model adaptation. HEISIR implements a two-step process: (1) Hierarchical Triplets Formulation and (2) Adjunct Augmentation, creating semantic indices consisting of Subject-Verb-Object-Adjunct (SVOA) quadruplets. This structured representation effectively captures the underlying semantic information from dialogue content. HEISIR achieves high retrieval performance while maintaining low latency during the actual retrieval process. Our experimental results demonstrate that HEISIR outperforms fine-tuned models across various embedding types and language models. Beyond improving retrieval capabilities, HEISIR also offers opportunities for intent and topic analysis in conversational data, providing a versatile solution for dialogue systems.
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