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Ask HN: Any insider takes on Yann LeCun's push against current architectures?

385 points vessenes | 1 comments | 10 Mar 25 19:41 UTC | HN request time: 0.213s | source

So, Lecun has been quite public saying that he believes LLMs will never fix hallucinations because, essentially, the token choice method at each step leads to runaway errors -- these can't be damped mathematically.

In exchange, he offers the idea that we should have something that is an 'energy minimization' architecture; as I understand it, this would have a concept of the 'energy' of an entire response, and training would try and minimize that.

Which is to say, I don't fully understand this. That said, I'm curious to hear what ML researchers think about Lecun's take, and if there's any engineering done around it. I can't find much after the release of ijepa from his group.

1. trod1234 ◴[15 Mar 25 04:43 UTC] No.43370048[source]▶
Not an ML researcher, but neither of those ideas are going to work.

The token approach is inherently flawed because the tokens pre-suppose unique meaning when in fact they may not be unique.

Said another way, it lacks properties that would be able to differentiate true from false because the differentiating input isn't included and cannot be derived from the inputs given. This goes to decidability.