# An Application of Large Language Models to Coding Negotiation Transcripts

arXiv: https://arxiv.org/abs/2407.21037 · HTML: https://mlsanity.com/paper/2407.21037/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Full Parameter Fine-tuning for Large Language Models with Limited Resources](https://mlsanity.com/paper/2306.09782/) (2023)
- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)

## Built on by (most cited first, 2 of 2)

- [Emotionally-Aware Agents for Dispute Resolution](https://mlsanity.com/paper/2509.04465/) (2025)
- [PieArena: Ranking and Profiling Language Agents in Realistic Negotiation Scenarios](https://mlsanity.com/paper/2602.05302/) (2026)

## Abstract (arXiv)

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.