An Application of Large Language Models to Coding Negotiation Transcripts

• Published in: arXiv.org
• Main Authors: Friedman, Ray, Cho, Jaewoo, Brett, Jeanne, Zhan, Xuhui, Han, Ningyu, Kannan, Sriram, Ma, Yingxiang, Spencer-Smith, Jesse, Jäckel, Elisabeth, Zerres, Alfred, Hooper, Madison, Babbit, Katie, Acharya, Manish, Adair, Wendi, Aslani, Soroush, Aykaç, Tayfun, Bauman, Chris, Bennett, Rebecca, Brady, Garrett, Briggs, Peggy, Dowie, Cheryl, Eck, Chase, Geiger, Igmar, Jacob, Frank, Kern, Molly, Lee, Sujin, Liu, Leigh Anne, Liu, Wu, Loewenstein, Jeffrey, Lytle, Anne, Li, Ma, Mann, Michel, Mislin, Alexandra, Tyree, Mitchell, Hannah Martensen née Nagler, Nandkeolyar, Amit, Olekalns, Mara, Paliakova, Elena, Parlamis, Jennifer, Pierce, Jason, Pierce, Nancy, Pinkley, Robin, Prime, Nathalie, Ramirez-Marin, Jimena, Rockmann, Kevin, Ross, William, Semnani-Azad, Zhaleh, Schroeder, Juliana, Smith, Philip, Stimmer, Elena, Swaab, Roderick, Thompson, Leigh, Tinsley, Cathy, Tuncel, Ece, Weingart, Laurie, Wilken, Robert, Yao, JingJing, Zhi-Xue, Zhang
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 18.07.2024
• Subjects: Coding; Large language models; Natural language processing; Negotiations; Zero-shot learning
• ISSN: 2331-8422

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.