Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination
This addresses the hallucination problem in LLMs for finance applications, but it is incremental as it focuses on empirical validation and evaluation of existing methods.
The paper empirically examines hallucination behaviors of large language models (LLMs) in financial tasks, finding that off-the-shelf LLMs experience serious hallucination, and evaluates four methods to alleviate it, though specific numerical results are not provided.
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.