  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.
