Most academic work assumes that hallucination is a binary feature: either something is a hallucination or it is not a hallucination. But this is too simplistic. In real-world contexts we see many subtleties, eg some hallucinations are much more damaging than others, statements which are literally true can still mislead readers because of context, and there are many borderline cases.

I recently published a paper and gave a talk about evaluating real-world impact. I got some great feedback from this, and summarise some of the suggested papers (including more examples of impact eval) and insightful comments (eg, about eval “ecosystem”) I received.

Most LLM benchmarks and leaderboards are garbage. Unfortunately, it now seems that even the few “good” benchmarks (such as SWEBench and Chatbot Arena) are compromised because they are being gamed by the big LLM vendors, who tweak the benchmarks and rules so that their systems do better.