让检索模型自动判断文档字段重要性，提升结构化文本搜索效果
Multi-Field Adaptive Retrieval
- 将文档拆解为标题、正文等字段，分别用稠密和词法方法独立索引
- 根据查询动态调整各字段权重，显著优于现有检索器
- 适合处理带格式的结构化文档，如网页、论文、数据库记录
针对搜索与检索增强生成任务中常见无结构自由文本的局限，本文提出多字段自适应检索（MFAR）框架，适用于包含标题、正文、HTML头等结构化字段的文档。该框架分两步：首先将文档分解为多个字段，每个字段独立采用稠密和词法方法索引；其次训练一个模型，基于查询动态预测各字段的重要性，实现运行时字段加权。实验表明，该方法能优化不同字段类型上稠密与词法表示的使用，显著提升文档排序效果，在多字段结构化数据上达到当前最优性能。
原文摘要 · Abstract (English)
Document retrieval for tasks such as search and retrieval-augmented generation typically involves datasets that are unstructured: free-form text without explicit internal structure in each document. However, documents can have a structured form, consisting of fields such as an article title, message body, or HTML header. To address this gap, we introduce Multi-Field Adaptive Retrieval (MFAR), a flexible framework that accommodates any number of and any type of document indices on structured data. Our framework consists of two main steps: (1) the decomposition of an existing document into fields, each indexed independently through dense and lexical methods, and (2) learning a model which adaptively predicts the importance of a field by conditioning on the document query, allowing on-the-fly weighting of the most likely field(s). We find that our approach allows for the optimized use of dense versus lexical representations across field types, significantly improves in document ranking over a number of existing retrievers, and achieves state-of-the-art performance for multi-field structured data.
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