Simon Willison’s Weblog

Is the LLM response wrong, or have you just failed to iterate it? (via) More from Mike Caulfield (see also the SIFT method). He starts with a fantastic example of Google's AI mode usually correctly handling a common piece of misinformation but occasionally falling for it (the curse of non-deterministic systems), then shows an example if what he calls a "sorting prompt" as a follow-up:

What is the evidence for and against this being a real photo of Shirley Slade?

The response starts with a non-committal "there is compelling evidence for and against...", then by the end has firmly convinced itself that the photo is indeed a fake. It reads like a fact-checking variant of "think step by step".

Mike neatly describes a problem I've also observed recently where "hallucination" is frequently mis-applied as meaning any time a model makes a mistake:

The term hallucination has become nearly worthless in the LLM discourse. It initially described a very weird, mostly non-humanlike behavior where LLMs would make up things out of whole cloth that did not seem to exist as claims referenced any known source material or claims inferable from any known source material. Hallucinations as stuff made up out of nothing. Subsequently people began calling any error or imperfect summary a hallucination, rendering the term worthless.

In this example is the initial incorrect answers were not hallucinations: they correctly summarized online content that contained misinformation. The trick then is to encourage the model to look further, using "sorting prompts" like these:

• Facts and misconceptions and hype about what I posted
• What is the evidence for and against the claim I posted
• Look at the most recent information on this issue, summarize how it shifts the analysis (if at all), and provide link to the latest info

I appreciated this closing footnote:

Should platforms have more features to nudge users to this sort of iteration? Yes. They should. Getting people to iterate investigation rather than argue with LLMs would be a good first step out of this mess that the chatbot model has created.