通过混合结构摘要与基于LLM的数据增强改进主题相关性模型
Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation
AI总结：
本文通过混合结构摘要与基于LLM的数据增强方法，提升社交搜索中查询与文档的主题相关性建模性能。
摘要：
查询与文档之间的主题相关性是社交搜索中非常重要的一部分，可以评估文档与用户需求之间的匹配程度。在大多数社交搜索场景中，如大众点评，建模搜索相关性始终面临两个挑战。一个是许多社交搜索文档非常长且包含大量冗余信息。另一个是搜索相关性模型的训练数据难以获得，特别是对于多分类相关性模型。为了解决上述两个问题，我们首先将查询与基于查询的摘要以及不包含查询的文档摘要作为主题相关性模型的输入，这有助于模型学习查询与文档核心主题之间的相关程度。然后，我们利用大语言模型（LLM）的理解和生成能力，从现有训练数据中的查询和文档中重写和生成查询，从而构建新的查询-文档对作为训练数据。广泛的离线实验和在线A/B测试表明，所提出的方法有效提高了相关性建模的性能。
英文摘要：
Topic relevance between query and document is a very important part of social search, which can evaluate the degree of matching between document and user's requirement. In most social search scenarios such as Dianping, modeling search relevance always faces two challenges. One is that many documents in social search are very long and have much redundant information. The other is that the training data for search relevance model is difficult to get, especially for multi-classification relevance model. To tackle above two problems, we first take query concatenated with the query-based summary and the document summary without query as the input of topic relevance model, which can help model learn the relevance degree between query and the core topic of document. Then, we utilize the language understanding and generation abilities of large language model (LLM) to rewrite and generate query from queries and documents in existing training data, which can construct new query-document pairs as training data. Extensive offline experiments and online A/B tests show that the proposed approaches effectively improve the performance of relevance modeling.
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。
