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An Application of Large Language Models to Coding Negotiation Transcripts

18 July 2024
Ray Friedman
Jaewoo Cho
Jeanne Brett
Xuhui Zhan
Ningyu Han
Sriram Kannan
Yingxiang Ma
Jesse Spencer-Smith
Elisabeth Jäckel
Alfred Zerres
Madison Hooper
Katie Babbit
Manish Acharya
Wendi L. 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 L. Lytle
Li Ma
Michel Mann
Alexandra A. Mislin
Tyree Mitchell
Hannah Martensen née Nagler
Amit K. Nandkeolyar
Mara Olekalns
Elena Paliakova
Jennifer Parlamis
Jason Pierce
Nancy Pierce
Robin L. Pinkley
N. Prime
Jimena Y. Ramirez-Marin
Kevin W. Rockmann
William Ross
Zhaleh Semnani Azad
Juliana Schroeder
Philip Smith
Elena Stimmer
Roderick I. Swaab
Leigh Thompson
Cathy Tinsley
Ece Tuncel
Laurie R. Weingart
Robert Wilken
JingJing Yao
Zhi-Xue Zhang
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Abstract

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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