Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization capability of large language models to robustly represent complex conversational sessions for dense retrieval. To achieve this, we propose a simple and effective dual-learning approach that adapts LLM for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning on high-quality conversational instruction tuning data. Extensive experiments on five conversational search benchmarks demonstrate that ChatRetriever substantially outperforms existing conversational dense retrievers, achieving state-of-the-art performance on par with LLM-based rewriting approaches. Furthermore, ChatRetriever exhibits superior robustness in handling diverse conversational contexts. Our work highlights the potential of adapting LLMs for retrieval with complex inputs like conversational search sessions and proposes an effective approach to advance this research direction.

翻译：对话式搜索需要从复杂的多轮上下文中准确理解用户意图。本文提出ChatRetriever，它继承大型语言模型的强大泛化能力，能够鲁棒地表示复杂对话会话以支持稠密检索。为此，我们提出一种简单有效的双学习框架：通过对比学习适配LLM进行检索，同时利用高质量对话指令微调数据，通过掩码指令微调增强对复杂会话的理解。在五个对话式搜索基准上的实验表明，ChatRetriever显著优于现有对话式稠密检索器，达到与基于LLM的重写方法相当的先进性能。此外，ChatRetriever在处理多样化对话上下文时展现出更强的鲁棒性。本研究揭示了将LLM适配于对话式搜索等复杂输入检索任务的潜力，并提出了一种推动该研究方向发展的有效方法。