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An Application of Large Language Models to Coding Negotiation Transcripts

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arxiv 2407.21037 v1 pith:CCIK564F submitted 2024-07-18 cs.CL cs.AI

Ray Friedman , Jaewoo Cho , Jeanne Brett , Xuhui Zhan , Ningyu Han , Sriram Kannan , Yingxiang Ma , Jesse Spencer-Smith
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Elisabeth Jäckel Alfred Zerres Madison Hooper Katie Babbit Manish Acharya Wendi Adair Soroush Aslani Tayfun Aykaç Chris Bauman Rebecca Bennett Garrett Brady Peggy Briggs Cheryl Dowie Chase Eck Igmar Geiger Frank Jacob Molly Kern Sujin Lee Leigh Anne Liu Wu Liu Jeffrey Loewenstein Anne Lytle Li Ma Michel Mann Alexandra Mislin Tyree Mitchell Hannah Martensen née Nagler Amit Nandkeolyar Mara Olekalns Elena Paliakova Jennifer Parlamis Jason Pierce Nancy Pierce Robin Pinkley Nathalie Prime Jimena Ramirez-Marin Kevin Rockmann William Ross Zhaleh Semnani-Azad Juliana Schroeder Philip Smith Elena Stimmer Roderick Swaab Leigh Thompson Cathy Tinsley Ece Tuncel Laurie Weingart Robert Wilken JingJing Yao Zhi-Xue Zhang
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classification cs.CLcs.AI
keywords llmslanguagemodelsnegotiationapplicationappliedcodinglarge
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent years, Large Language Models (LLM) have demonstrated impressive capabilities in the field of natural language processing (NLP). This paper explores the application of LLMs in negotiation transcript analysis by the Vanderbilt AI Negotiation Lab. Starting in September 2022, we applied multiple strategies using LLMs from zero shot learning to fine tuning models to in-context learning). The final strategy we developed is explained, along with how to access and use the model. This study provides a sense of both the opportunities and roadblocks for the implementation of LLMs in real life applications and offers a model for how LLMs can be applied to coding in other fields.

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  1. Emotionally-Aware Agents for Dispute Resolution

    cs.CL 2025-08 conditional novelty 6.0 of 10

    LLM emotion labeling of dispute dialogues explains up to ~40% of variance in subjective outcomes (vs ~5% in prior negotiation work) and reveals anger escalation and compassion de-escalation patterns.

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