FishBargain: An LLM-Empowered Bargaining Agent for Online Fleamarket Platform Sellers

TL;DR

FishBargain is an LLM-powered bargaining agent designed for online fleamarket sellers, enabling more effective negotiations by understanding context, strategizing actions, and generating appropriate dialogue, thereby increasing successful deals.

Contribution

This paper introduces FishBargain, the first LLM-based proactive bargaining agent tailored for online fleamarket sellers, addressing the challenge of dynamic negotiations.

Findings

FishBargain helps sellers secure more deals in real-world tests.

Qualitative analysis shows effective understanding and strategy generation.

Quantitative results indicate increased success rate of negotiations.

Abstract

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.