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.

ChatRetriever 是一个通过对比学习调整的对话式检索模型，能够准确解读用户意图并表示复杂对话会话，通过在高质量对话指令数据上进行掩码训练进一步提升对话理解，实验证明在五个对话式检索基准上 ChatRetriever 显著优于现有模型，在复杂对话上表现出卓越的鲁棒性，展示了适应 LLM 的潜力并推进了对话式检索研究的发展方向。