Abstract
The growing prominence of LLMs has led to an increase in the development of AI tutoring systems. These systems are crucial in providing underrepresented populations with improved access to valuable education. One important area of education that is unavailable to many learners is strategic bargaining related to negotiation. To address this, we develop a LLM-based Assistant for Coaching nEgotiation (ACE). ACE not only serves as a negotiation partner for users but also provides them with targeted feedback for improvement. To build our system, we collect a dataset of negotiation transcripts between MBA students. These transcripts come from trained negotiators and emulate realistic bargaining scenarios. We use the dataset, along with expert consultations, to design an annotation scheme for detecting negotiation mistakes. ACE employs this scheme to identify mistakes and provide targeted feedback to users. To test the effectiveness of ACE-generated feedback, we conducted a user experiment with two consecutive trials of negotiation and found that it improves negotiation performances significantly compared to a system that doesn’t provide feedback and one which uses an alternative method of providing feedback.
- Collected a dataset of negotiation transcripts from an MBA negotiation class
- Develop annotation scheme to identify and correct mistakes users make in negotiation (8 categories, can be divided into preparation errors and negotiation errors)
- Strategic walk-away point: maximum amount they would pay to purchase the item
- Strategic target price: target price that buyer sets before negotiation
- Breaking the ice
- Giving the first offer
- Ambitious opening point
- Strong counteroffer
- Including rationale
- Strategic closing behavior
- Build ACE to the scheme, Such that it uses the annotation categories to identify users’ mistakes and then provide targeted feedback based on the error definitions