用相关性反馈重构检索，零样本下更准更快。
Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
- 让大模型判断文档相关性，只输出一个词，避免生成冗长假文档。
- 在多个低资源数据集上超越当前最佳零样本检索方法。
- 适合无标注数据且对响应速度敏感的检索场景。
当缺乏相关性标注时，构建有效的密集检索系统仍具挑战。现有方法利用大语言模型（LLM）生成假设文档以找到最接近的真实文档，但该方法依赖LLM具备领域知识，且生成大量文本导致效率低下。为此，本文提出真实文档嵌入的相关性反馈机制（ReDE-RF）。受相关性反馈启发，将假设文档生成重构为相关性估计任务：让LLM仅判断哪些真实文档应参与最近邻搜索。此方法使LLM无需领域知识，只需输出单个标记，显著降低查询延迟。实验表明，ReDE-RF在多种低资源检索数据集上持续优于当前最优零样本密集检索方法，同时大幅减少每查询延迟。
原文摘要 · Abstract (English)
Building effective dense retrieval systems remains difficult when relevance supervision is not available. Recent work has looked to overcome this challenge by using a Large Language Model (LLM) to generate hypothetical documents that can be used to find the closest real document. However, this approach relies solely on the LLM to have domain-specific knowledge relevant to the query, which may not be practical. Furthermore, generating hypothetical documents can be inefficient as it requires the LLM to generate a large number of tokens for each query. To address these challenges, we introduce Real Document Embeddings from Relevance Feedback (ReDE-RF). Inspired by relevance feedback, ReDE-RF proposes to re-frame hypothetical document generation as a relevance estimation task, using an LLM to select which documents should be used for nearest neighbor search. Through this re-framing, the LLM no longer needs domain-specific knowledge but only needs to judge what is relevant. Additionally, relevance estimation only requires the LLM to output a single token, thereby improving search latency. Our experiments show that ReDE-RF consistently surpasses state-of-the-art zero-shot dense retrieval methods across a wide range of low-resource retrieval datasets while also making significant improvements in latency per-query.
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