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arXiv · 2509.06341

Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent

Issue Yishu Wang·Kakam Chong·Xiaofeng Wang·Xu Yan·DeXin Kong·Chen Ju·Ming Chen·Shuai Xiao·Shuguang Han·jufeng chen
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Abstract

In online second-hand marketplaces, multi-turn bargaining is a crucial part of seller-buyer interactions. Large Language Models (LLMs) can act as seller agents, negotiating with buyers on behalf of sellers under given business constraints. A critical ability for such agents is to track and accurately interpret cumulative buyer intents across long negotiations, which directly impacts bargaining effectiveness. We introduce a multi-turn evaluation framework for measuring the bargaining ability of seller agents in e-commerce dialogues. The framework tests whether an agent can extract and track buyer intents. Our contributions are: (1) a large-scale e-commerce bargaining benchmark spanning 622 categories, 9,892 products, and 3,014 tasks; (2) a turn-level evaluation framework grounded in Theory of Mind (ToM) with annotated buyer intents, moving beyond outcome-only metrics; and (3) an automated pipeline that extracts reliable intent from massive dialogue data.

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Issue Yishu Wang, Kakam Chong, Xiaofeng Wang, Xu Yan, DeXin Kong, Chen Ju, Ming Chen, Shuai Xiao, Shuguang Han, jufeng chen. 2025-09-08. Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent. https://arxiv.org/abs/2509.06341

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