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ACE: A LLM-based Negotiation Coaching System

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arxiv 2410.01555 v1 pith:335AV6SU submitted 2024-10-02 cs.CL cs.HC

Ryan Shea , Aymen Kallala , Xin Lucy Liu , Michael W. Morris , Zhou Yu This is my paper · ORCID
classification cs.CLcs.HC
keywords negotiationfeedbacksystembargainingcoachingdataseteducationllm-based
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies

    cs.CL 2026-08 conditional novelty 6.0 of 10

    Synthetic clinical communication generated by LLMs can train clinical NLP models in thirteen case studies, but only one is tested on real patient text, leaving transfer to authentic communication unproven.

  2. EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation

    cs.AI 2025-09 reject novelty 6.0 of 10

    EvoEmo evolves emotion-transition policies for buyer LLM agents and reports higher savings, success rates, and efficiency than vanilla or fixed-emotion baselines in simulated price negotiations.

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