An Application of Large Language Models to Coding Negotiation Transcripts

TL;DR

This paper investigates how large language models can be applied to analyze negotiation transcripts, exploring various strategies and discussing potential opportunities and challenges for real-world implementation.

Contribution

It introduces a comprehensive approach to applying LLMs to negotiation transcript analysis, including multiple strategies from zero-shot to fine-tuning, and provides a practical model for similar applications.

Findings

Developed a final strategy for LLM application in negotiation analysis

Identified opportunities for LLM use in real-world negotiation scenarios

Discussed roadblocks and challenges in deploying LLMs in practice

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.