Understanding and mitigating LLM “hallucinations” in financial analysis and data visualization

LLMs, such as OpenAI’s GPT series, can sometimes generate responses that are referred to as “hallucinations.” These are instances where the output from the model is factually incorrect, it presents information that it could not possibly know (given it doesn’t have access to real-time or personalized data), or it might output something nonsensical or highly improbable.

Let’s explore deeper into what hallucinations are, how to identify them, and what steps can be taken to mitigate their impact, especially in a context where accurate and reliable information is crucial, such as financial analysis, trading, or visual data presentations.

Understanding hallucinations

Let’s look at some examples:

• Factual inaccuracies: Suppose an LLM provides information stating that Apple Inc. was founded in 1985. This is a clear factual inaccuracy...