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.
@article{arxiv.2509.06341,
title = {Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent},
author = {Issue Yishu Wang and Kakam Chong and Xiaofeng Wang and Xu Yan and DeXin Kong and Chen Ju and Ming Chen and Shuai Xiao and Shuguang Han and jufeng chen},
journal= {arXiv preprint arXiv:2509.06341},
year = {2025}
}