Zero is Not Hero Yet: Benchmarking Zero-Shot Performance of LLMs for Financial Tasks

TL;DR

This paper evaluates the zero-shot capabilities of large language models like ChatGPT in financial tasks, comparing them to fine-tuned models and discussing annotation challenges, providing insights into their effectiveness and limitations.

Contribution

It offers a comprehensive benchmark of zero-shot LLM performance in finance, highlighting the strengths and weaknesses of generative models versus fine-tuned models.

Findings

ChatGPT performs well without labeled data

Fine-tuned models generally outperform ChatGPT

Annotating with generative models is time-consuming

Abstract

Recently large language models (LLMs) like ChatGPT have shown impressive performance on many natural language processing tasks with zero-shot. In this paper, we investigate the effectiveness of zero-shot LLMs in the financial domain. We compare the performance of ChatGPT along with some open-source generative LLMs in zero-shot mode with RoBERTa fine-tuned on annotated data. We address three inter-related research questions on data annotation, performance gaps, and the feasibility of employing generative models in the finance domain. Our findings demonstrate that ChatGPT performs well even without labeled data but fine-tuned models generally outperform it. Our research also highlights how annotating with generative models can be time-intensive. Our codebase is publicly available on GitHub under CC BY-NC 4.0 license.