Would you like to use large language models (LLMs) in your own project, but are troubled by their tendency to frequently “hallucinate”, or produce incorrect information? Have you ever wondered if there was a way to easily measure an LLM’s hallucination rate, and compare this against other models? And would you like to learn how to help LLMs produce more accurate information? In this talk, we’ll have a look at some of the main reasons that hallucinations occur in LLMs, and then focus on how we can measure one specific type of hallucination: the tendency of models to regurgitate misinformation that they have learned from their training data. We’ll explore how we can easily measure this type of hallucination in LLMs using a dataset called TruthfulQA in conjunction with Python tooling including Hugging Face’s datasets, and LangChain. We’ll end by looking at initiatives to reduce hallucinations in LLMs, and how complex this can be.