The launch of ever-capable large language models (LLMs) such as GPT-3.5 has sparked much interest over the past six months. However, trust in these models has waned as users have discovered they can make mistakes – and that, just like us, they aren’t perfect.
An LLM that outputs incorrect information is said to be “hallucinating”, and there is now a growing research effort towards minimising this effect. But as we grapple with this task, it’s worth reflecting on our own capacity for bias and hallucination – and how this impacts the accuracy of the LLMs we create.
