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I've never understood this critique. Models have the capability to say: "oh, I made a mistake here, let me change this" and that solves the issue, right? A little bit of engineering and fine tuning - you could imagine a model producing a sequence of statements, and reflecting on the sequence - updating things like "statement 7, modify: xzy to xyz"
Attention works, yes. But it is not naturally plausible at all. We don't do quadratic comparisons across a whole book or need to see thousands of samples to understand.
Personally I think that in the future recursive architectures and test time training will have a better chance long term than current full attention.
Also, I think that OpenAI biggest contribution is demostrating that reasoning like behaviors can emerge from really good language modelling.
The paper would be a strong argument against your point: if neural architectures are already constraining the amount of information that a text generation system delivers the same way a human (allegedly) does, then I don't see which "energy" measure one could take that could perform any better.
Then again, perhaps they have one in mind and I just haven't read it.
Sligtly related: Energy Based Models (EBMs) are better in theory and yet too resource intensive. I tried to sell using EBMs to my org, but the price for even a small use case was prohibitive. I learned it from: https://youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyo... Yann LeCun, and Michael Bronstein and his colleagues have some similarities in trying to properly Sciencify Deep Learning. Yann LeCun's approach,…
Earlier quoted context omitted.
The problem is exactly that: the probability distribution. The network has no way to say: 0% everyone, this is non sense, backtrack everything. Other architectures, like energy based models or bayesian ones can assess uncertainty. Transformers simply cannot do it (yet). Yes, there are ways to do it, but we are already spending millions to get coherent phrases, few ones will burn billions to train a model that can do…
Has anybody ever messed with adding a "backspace" token?
There's a quite intense backlog of new stuff that hasn't made it to prod. (I would have told you in 2023 that we would have ex. switched to Mamba-like architectures in at least one leading model)
Broadly, it's probably unhelpful that:
- absolutely no one wants the PR of releasing a model that isn't competitive with the latest peers
- absolutely everyone wants to release an incremental improvement, yesterday
- Entities with no PR constraint, and no revenue repurcussions when reallocating funds from surely-productive to experimental, don't show a significant improvement in results for the new things they try (I'm thinking of ex. Allen Institute)
Another odd property I can't quite wrap my head around is the battlefield is littered with corpses that eval okay-ish, and should have OOM increases in some areas (I'm thinking of RWKV, and how it should be faster at inference), and they're not really in the conversation either.
Makes me think either A) I'm getting old and don't really understand ML from a technical perspective anyway or B) hey, I 've been maintaining a llama.cpp wrapper that works on every platform for a year now, I should trust my instincts: the real story is UX is king and none of these things actually improve the experience of a user even if benchmarks are ~=.
I haven't read Yann Lecun's take. Based on your description alone my first impression would be: there's a paper [1] arguing that "beam search enforces uniform information density in text, a property motivated by cognitive science". UID claims, in short, that a speaker only delivers as much content as they think the listener can take (no more, no less) and the paper claims that beam search enforced this property at ge…
Earlier quoted context omitted.
> The problem with LLMs is that the output is inherently stochastic - i.e there isn't a "I don't have enough information" option. This is due to the fact that LLMs are basically just giant look up maps with interpolation. I don't think this explanation is correct. The input to the decoder at the end of all the attention heads etc (as I understand it) is a probability distribution over tokens. So the model as a whole…
Right. And, as a result, low token-level confidence can end up indicating "there are other ways this could have been worded" or "there are other topics which could have been mentioned here" just as often as it does "this output is factually incorrect". Possibly even more often, in fact.
Sligtly related: Energy Based Models (EBMs) are better in theory and yet too resource intensive. I tried to sell using EBMs to my org, but the price for even a small use case was prohibitive. I learned it from: https://youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyo... Yann LeCun, and Michael Bronstein and his colleagues have some similarities in trying to properly Sciencify Deep Learning. Yann LeCun's approach,…