用大模型帮个人卖家在线讨价还价，提升成交率。
FishBargain: An LLM-Empowered Bargaining Agent for Online Fleamarket Platform Sellers
- 基于大模型构建主动谈判代理，理解上下文并预判对手策略。
- 在闲鱼平台实测，显著提升个人卖家的成交数量。
- 适合缺乏经验的个人卖家快速上手在线议价。
与传统电商（如亚马逊）不同，在线跳蚤市场（如Craigslist）主要面向时间有限、商业经验不足的个人卖家。这些卖家常因不擅长议价而无法完成交易。尽管大语言模型（LLMs）在各类对话任务中展现出巨大潜力，但现有应用多为被动响应用户指令。议价作为主动型对话任务，需应对环境动态变化和对手策略不确定性。本文提出针对跳蚤市场卖家的LLM赋能谈判代理FishBargain。该代理能理解聊天上下文与商品信息，综合考虑对手可能行为，选择行动与语言策略并生成回应。FishBargain已在中文最大跳蚤市场平台闲鱼（Xianyu）上对数千名个人卖家进行测试。定性和定量实验均表明，该系统能有效帮助卖家促成更多交易。
原文摘要 · Abstract (English)
Different from traditional Business-to-Consumer e-commerce platforms~(e.g., Amazon), online fleamarket platforms~(e.g., Craigslist) mainly focus on individual sellers who are lack of time investment and business proficiency. Individual sellers often struggle with the bargaining process and thus the deal is unaccomplished. Recent advancements in Large Language Models(LLMs) demonstrate huge potential in various dialogue tasks, but those tasks are mainly in the form of passively following user's instruction. Bargaining, as a form of proactive dialogue task, represents a distinct art of dialogue considering the dynamism of environment and uncertainty of adversary strategies. In this paper, we propose an LLM-empowered bargaining agent designed for online fleamarket platform sellers, named as FishBargain. Specifically, FishBargain understands the chat context and product information, chooses both action and language skill considering possible adversary actions and generates utterances. FishBargain has been tested by thousands of individual sellers on one of the largest online fleamarket platforms~(Xianyu) in China. Both qualitative and quantitative experiments demonstrate that FishBargain can effectively help sellers make more deals.
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