召回在衡量检索质量时有多重要？
How important is Recall for Measuring Retrieval Quality?
AI总结：
本文评估了在大型动态知识库中，如何通过LLM判断响应质量来衡量检索质量，提出了一种无需知道总相关文档数的高效方法。
摘要：
在大型动态知识库的现实检索设置中，查询相关的总文档数通常未知，因此无法计算召回率。本文评估了几种处理这一限制的已建立策略，通过测量检索质量指标与基于LLM的响应质量判断之间的相关性来处理这一问题。我们跨多个数据集进行了实验，这些数据集中的相关文档数量相对较少（2-15）。我们还介绍了一种简单的检索质量度量方法，该方法在不需知道总相关文档数的情况下表现良好。
英文摘要：
In realistic retrieval settings with large and evolving knowledge bases, the total number of documents relevant to a query is typically unknown, and recall cannot be computed. In this paper, we evaluate several established strategies for handling this limitation by measuring the correlation between retrieval quality metrics and LLM-based judgments of response quality, where responses are generated from the retrieved documents. We conduct experiments across multiple datasets with a relatively low number of relevant documents (2-15). We also introduce a simple retrieval quality measure that performs well without requiring knowledge of the total number of relevant documents.
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。
