ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questions
Abstract: 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 **PRPCLARE** 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.
Paper Type: Long
Research Area: Dialogue and Interactive Systems
Research Area Keywords: LLM-based Agent, Conversational Seeking, Information Retrieval
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 2476
Loading