Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914027317231616 |
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| author | Wang, Issue Yishu Chong, Kakam Wang, Xiaofeng Yan, Xu Kong, DeXin Ju, Chen Chen, Ming Xiao, Shuai Han, Shuguang chen, jufeng |
| author_facet | Wang, Issue Yishu Chong, Kakam Wang, Xiaofeng Yan, Xu Kong, DeXin Ju, Chen Chen, Ming Xiao, Shuai Han, Shuguang chen, jufeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06341 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent Wang, Issue Yishu Chong, Kakam Wang, Xiaofeng Yan, Xu Kong, DeXin Ju, Chen Chen, Ming Xiao, Shuai Han, Shuguang chen, jufeng Artificial Intelligence 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. |
| title | Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.06341 |