Earnings Call Scripts Generation With Large Language Models Using Few-Shot Learning Prompt Engineering and Fine-Tuning Methods

Abstract

Company earnings calls are pivotal events that offer crucial insights into a company's financial well-being and future outlook. Large language models (LLMs) present a promising avenue for automatically generating the initial draft of earnings call scripts, leveraging financial data and past examples. We evaluate two distinct methods: (1) few-shot learning prompt engineering with a large language model (LLM) and (2) fine-tuning a large language model on earnings call transcript data. Our findings indicate that both methods can produce coherent scripts encompassing key metrics, updates, and guidance. However, there are inherent trade-offs in comprehensiveness, potential hallucinations, writing style, ease of use, and cost. We discuss the pros and cons of each method to guide practitioners on effectively harnessing LLMs for earnings call script generation. Notably, we employ a human and two different LLMs to act as judges to compare the outcomes generated by the two approaches.