用段落查询提升对话式搜索的文档匹配效果。
Passage Query Methods for Retrieval and Reranking in Conversational Agents
- 生成与目标文档格式对齐的段落查询，增强检索匹配度。
- 短长段落法在多轮对话中表现优于基线，媲美GPT-4。
- 适合研究对话系统检索与重排序的开发者参考。
本文介绍了我们在TREC互动知识辅助任务（iKAT）中的方法，旨在提升对话式信息获取（CIS）系统的性能。尽管近期进展提升了对话代理的理解能力，但在跨领域、多轮对话中理解上下文并准确检索文档仍面临挑战。为此，我们扩展了生成-检索-再生成流程，提出段落查询（Passage Queries, PQs），使其与目标文档的预期格式对齐，以改善检索阶段的查询-文档匹配。提出了两种变体：加权重排序和短长段落法，均采用Meta Llama模型进行上下文理解与查询生成。段落排序评估结果显示，短长段落法优于组织方基线，在所有基于Llama的系统中表现最佳，并达到与GPT-4相当的效果。结果表明，该方法在效率与性能间取得良好平衡，段落查询能有效提升语义对齐性，具有提升多轮对话系统的能力。
原文摘要 · Abstract (English)
This paper presents our approach to the TREC Interactive Knowledge Assistance Track (iKAT), which focuses on improving conversational information-seeking (CIS) systems. While recent advancements in CIS have improved conversational agents' ability to assist users, significant challenges remain in understanding context and retrieving relevant documents across domains and dialogue turns. To address these issues, we extend the Generate-Retrieve-Generate pipeline by developing passage queries (PQs) that align with the target document's expected format to improve query-document matching during retrieval. We propose two variations of this approach: Weighted Reranking and Short and Long Passages. Each method leverages a Meta Llama model for context understanding and generating queries and responses. Passage ranking evaluation results show that the Short and Long Passages approach outperformed the organizers' baselines, performed best among Llama-based systems in the track, and achieved results comparable to GPT-4-based systems. These results indicate that the method effectively balances efficiency and performance. Findings suggest that PQs improve semantic alignment with target documents and demonstrate their potential to improve multi-turn dialogue systems.
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