An Application of Large Language Models to Coding Negotiation Transcripts
This work addresses the challenge of coding negotiation transcripts for researchers and practitioners, but it is incremental as it applies existing LLM methods to a new domain.
The paper tackled the problem of analyzing negotiation transcripts by applying Large Language Models (LLMs), resulting in the development of a model through strategies like zero-shot learning and fine-tuning, with insights into opportunities and roadblocks for real-life implementation.
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.