[dartos]

> By being less distracted by irrelevant context, Diff Transformer can mitigate hallucination in question answering and text summarization

I’m very interested in this claim. I was under the impression that hallucination is unavoidable in these kinds of models. IIRC proof for that was trending on HN a couple weeks ago.

[pshc]

More broadly I think hallucination is inevitable in pure text models. We need model architectures incorporating a stream of real-world ground truth such as a live video feed or embodiment.

[moffkalast]

It's not possible to get rid of it entirely, but if you can get the model to bullshit only 0.1% of the time instead of 5% of the time it's a massive improvement.

Most of it should be happening when there's no data to draw conclusions from. E.g. STT models make up words in silence, vision models find things in lens cap noise, LLMs make up explanations when they have no data to pull from.

The real solution would be more along the lines of training models to specifically ignore these cases, or in the case of LLMs to just know when to say "I don't know".

[ErikBjare]

Mitigate, not completely fix.