Bargaining is an important and unique part of negotiation between humans. As
LLM-driven agents learn to negotiate and act like real humans, how to evaluate
agents' bargaining abilities remains an open problem. For the first time, we
formally described the Bargaining task as an asymmetric incomplete information
game, defining the gains of the Buyer and Seller in multiple bargaining
processes. It allows us to quantitatively assess an agent's performance in the
Bargain task. We collected a real product price dataset, AmazonHistoryPrice,
and conducted evaluations of various LLM agents' bargaining abilities. We find
that playing a Buyer is much harder than a Seller, and increasing model size
can not effectively improve the Buyer's performance. To address the challenge,
we propose a novel approach called OG-Narrator that integrates a deterministic
Offer Generator to control the price range of Buyer's offers, and an LLM
Narrator to create natural language sentences for generated offers.
Experimental results show that OG-Narrator improves the buyer's deal rates from
26.67% to 88.88% and brings a ten times of multiplication of profits on all
baselines, even a model that has not been aligned.

描述并量化了 LLM 驱动代理在具有不完全信息的买卖任务中的议价能力，并通过提出一种集成了确定性商品生成器和自然语言生成器的新方法 OG-Narrator 来提高买家的交易成功率和利润。