Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination

TL;DR

This paper empirically examines the hallucination problem of large language models in finance, revealing significant issues and evaluating methods to mitigate these hallucinations, highlighting the need for further research.

Contribution

It provides the first empirical analysis of LLM hallucinations in financial tasks and evaluates four methods to reduce hallucination effects.

Findings

LLMs exhibit serious hallucination behaviors in financial tasks

Current mitigation methods have limited effectiveness

Highlighting the urgent need for research to address hallucinations

Abstract

The hallucination issue is recognized as a fundamental deficiency of large language models (LLMs), especially when applied to fields such as finance, education, and law. Despite the growing concerns, there has been a lack of empirical investigation. In this paper, we provide an empirical examination of LLMs' hallucination behaviors in financial tasks. First, we empirically investigate LLM model's ability of explaining financial concepts and terminologies. Second, we assess LLM models' capacity of querying historical stock prices. Third, to alleviate the hallucination issue, we evaluate the efficacy of four practical methods, including few-shot learning, Decoding by Contrasting Layers (DoLa), the Retrieval Augmentation Generation (RAG) method and the prompt-based tool learning method for a function to generate a query command. Finally, our major finding is that off-the-shelf LLMs experience serious hallucination behaviors in financial tasks. Therefore, there is an urgent need to call for research efforts in mitigating LLMs' hallucination.