统一对话检索与回复生成，提升大模型对话系统效果
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations
- 联合微调使检索与生成任务协同优化
- 在五个数据集上同时提升检索与生成性能
- 适合构建端到端对话搜索系统的研究人员
对话式搜索系统通过多轮交互革新了信息获取方式。现有系统通常采用分离的检索与生成模型，限制了模型内在知识的协同利用，难以确保检索对生成的有效支持。现有统一模型研究未能充分解决对话上下文理解、独立检索管理及响应生成的问题。本文探索如何在对话中统一大语言模型的密集检索与响应生成。通过联合微调不同目标，并设计两种机制以降低不一致风险并缓解数据差异。在五个对话式搜索数据集上的评估表明，所提出的统一模型能相互促进两项任务，优于现有基线。
原文摘要 · Abstract (English)
The rapid advancement of conversational search systems revolutionizes how information is accessed by enabling the multi-turn interaction between the user and the system. Existing conversational search systems are usually built with two different models. This separation restricts the system from leveraging the intrinsic knowledge of the models simultaneously, which cannot ensure the effectiveness of retrieval benefiting the generation. The existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. In this paper, we explore how to unify dense retrieval and response generation for large language models in conversation. We conduct joint fine-tuning with different objectives and design two mechanisms to reduce the inconsistency risks while mitigating data discrepancy. The evaluations on five conversational search datasets demonstrate that our unified model can mutually improve both tasks and outperform the existing baselines.
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