This paper introduces the task of product demand clarification within an e-commercial scenario, where the user commences the conversation with ambiguous queries and the task-oriented agent is designed to achieve more accurate and tailored product searching by asking clarification questions. To address this task, we propose ProductAgent, a conversational information seeking agent equipped with abilities of strategic clarification question generation and dynamic product retrieval. Specifically, we develop the agent with strategies for product feature summarization, query generation, and product retrieval. Furthermore, we propose the benchmark called PROCLARE to evaluate the agent's performance both automatically and qualitatively with the aid of a LLM-driven user simulator. Experiments show that ProductAgent interacts positively with the user and enhances retrieval performance with increasing dialogue turns, where user demands become gradually more explicit and detailed. All the source codes will be released after the review anonymity period.

翻译：本文介绍了电子商务场景中的产品需求澄清任务，用户以模糊查询开启对话，而面向任务的智能体旨在通过提出澄清性问题来实现更准确、更个性化的产品搜索。针对此任务，我们提出了ProductAgent，一种具备策略性澄清问题生成和动态产品检索能力的对话式信息搜索智能体。具体而言，我们为该智能体开发了产品特征总结、查询生成和产品检索策略。此外，我们提出了名为PROCLARE的基准测试，借助基于大型语言模型的用户模拟器，对智能体性能进行自动和定性评估。实验表明，ProductAgent能与用户进行积极交互，并随着对话轮次增加（用户需求逐渐变得明确和详细）而提升检索性能。所有源代码将在评审匿名期结束后公开。