LLMs don't only hallucinate because of mistaken statements in their training data. It just comes hand-in-hand with the model's ability to remix, interpolate, and extrapolate answers to other questions that aren't directly answered in the dataset. For example if I ask ChatGPT a legal question, it might cite as precedent a case that doesn't exist at all (but which seems plausible, being interpolated from cases that do exist). It's not necessarily because it drew that case from a TV episode. It works the same way that GPT-3 wrote news releases that sounded convincing, matching the structure and flow of real articles.
Training only on factual data won't solve this.
Anyway, I can't help but feel saddened sometimes to see our talented people and investment resources being drawn in to developing these AI chatbots. These problems are solvable, but are we really making a better world by solving them?
Training only on factual data won't solve this.
Anyway, I can't help but feel saddened sometimes to see our talented people and investment resources being drawn in to developing these AI chatbots. These problems are solvable, but are we really making a better world by solving them?