因子化假设搜索用于证据到分类的检索

AI 摘要

Hugging Face 每日论文发布了一篇关于大型分类检索的研究，提出检索就绪差距问题，即输入为间接证据时目标概念难以被检索。为此，作者提出因子化假设搜索（FHS）方法，在金融分类标注和 CodiEsp 临床编码任务上取得了最佳性能。

正文节选

Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval Abstract Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however, the input is indirect evidence, such as a table cell whose meaning depends on its row, column, datatype, and context. We call this mismatch the retrieval readiness gap. Our analysis shows that the current index retrieves the target reliably when its semantics are explicit, while raw evidence often leaves it

发布时间：—

抓取时间：2026-08-11 12:38

来源机构：Hugging Face