I have mixed feelings about AI, but love the posts and papers that dig into how they work. Except, as this post shows, I seem to vastly prefer Anthropic's posts to OpenAI's.

> Claim: Hallucinations are inevitable.

> Finding: They are not, because language models can abstain when uncertain.

Please go back to your marketing cave. "Claim: You'll get wet if it rains. Finding: You will not, because you can check the weather report and get inside before it starts raining."

Sure, language models could abstain when uncertain. That would remove some hallucinations [a word which here means, make statements that are factually untrue. Never mind that that's often what we want them to do.] Or when certain about something that their training data is flawed or incomplete about. Or when certain about something but introspection shows that the chain of activations goes through territory that often produces hallucinations. Or when certain about something that is subjective.

"Uncertainty" is a loaded term; these things don't think in the way that the definition of the word "certain" is based on, since it's based on human thought. But that aside, LLM uncertainty is very obviously a promising signal to take into account, and it's interesting to see what costs and benefits that has. But eliminating one cause does not prove that there are no other causes, nor does it address the collateral damage.

"Write me a story about Bill."

"I'm sorry Dave, Bill is hypothetical and everything I could say about him would be a hallucination."

"Write a comment for the function `add(a, b) = a + b`."

"// This function takes two numbers and adds them toget... I'm sorry Dave, I don't know how many bits these numbers are, what the behavior on overflow is, or whether to include the results of extreme voltage fluctuations. As a result, I can't produce a comment that would be true in all circumstances and therefore any comment I write could be construed as a hallucination."