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Ask HN: Any insider takes on Yann LeCun's push against current architectures?
41–50 of 343 posts
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#42I am an MLE not an expert. However, it is a fundamental problem that our current paradigm of training larger and larger LLMs cannot ever scale to the precision people require for many tasks. Even in the highly constrained realm of chess, an enormous neural net will be outclassed by a small program that can run on your phone. https://arxiv.org/pdf/2402.04494
Any chance that “reasoning” can fix this
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#43Sincere question - why doesn't RL-based fine-tuning on top of LLMs solve this or at least push accuracy above a minimum acceptable threshhold in many use cases? OAI has a team doing this for enterprise clients. Several startups rolling out of current YC batch are doing versions of this.
If you mean the so called agentic AI, I don't think it's several. Iirc someone in the most recent demo day mentioned ~80%+ were AI
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#44I 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…
I believe he’s talking about some sort of ‘energy as measured by distance from the models understanding of the world’ as in quite literally a world model. But again I’m ignorant, hence the post!
In some respects that sounds similar to what we already do with reward models. I think with GRPO, the “bag of rewards” approach doesn’t strike me as terribly different. The challenge is in building out a sufficient “world” of rewards to adequately represent more meaningful feedback-based learning.
While it sounds nice to reframe it like a physics problem, it seems like a fundamentally flawed idea, akin to saying “there is a closed form solution to the question of how should I live.” The problem isn’t hallucinations, the problem is that language and relativism are inextricably linked.
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#45Not an official ML researcher, but I do happen to understand this stuff. 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. Energy minimization is more of an abstract approach to where you can use architectures that don't rely on things like different…
> 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.
Have you ever tried telling ChatGPT that you're "in the city centre" and asking it if you need to turn left or right to reach some landmark? It will not answer with the average of the directions given to everybody who asked the question before, it will answer asking you to tell it where you are precisely and which way you are facing.
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#46This concept comes from Hopfield networks.
If two nodes are on, but the connection between them is negative, this causes energy to be higher.
If one of those nodes switches off, energy is reduced.
With two nodes this is trivial. With 10 nodes it's more difficult to solve, and with billions of nodes it is impossible to "solve".
All you can do then is try to get the energy as low as possible.
This way also neural networks can find out "new" information, that they have not learned, but is consistent with the constraints they have learned about the world so far.
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#47[deleted]
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#48Not an official ML researcher, but I do happen to understand this stuff. 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. Energy minimization is more of an abstract approach to where you can use architectures that don't rely on things like different…
> 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…
I think some “reasoning” models do backtracking by inserting “But wait” at the start of a new paragraph? There’s more to it, but that seems like a pretty good trick.
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#49Well, it could be argued that the “optimal response” ie the one that sorta minimizes that “energy” is sorted by LLMs on the first iteration. And further iterations aren’t adding any useful information and in fact are countless occasions to veer off the optimal response.
For example if a prompt is: “what is the Statue of Liberty”, the LLMs first output token is going to be “the”, but it kinda already “knows” that the next ones are going to be “statue of liberty”.
So to me LLMs already “choose” a response path from the first token.
Conversely, a LLM that would try and find a minimum energy for the whole response wouldn’t necessarily stop hallucinating. There is nothing in the training of a model that says that “I don’t know” has a lower “energy” than a wrong answer…
Re: Ask HN: Any insider takes on Yann LeCun's push against current architectures?
#50Not an official ML researcher, but I do happen to understand this stuff. 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. Energy minimization is more of an abstract approach to where you can use architectures that don't rely on things like different…
> 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…
I feel like we're stacking naive misinterpretations of how LLMs function on top of one another here. Grasping gradient descent and autoregressive generation can give you a false sense of confidence. It is like knowing how transistors make up logic gates and believing you know more than CPU design than you actually do.
Rather than inferring from how you imagine the architecture working, you can look at examples and counterexamples to see what capabilities they have.
One misconception is that predicting the next word means there is no internal idea on the word after next. The simple disproof of this is that models put 'an' instead of 'a' ahead of words beginning with vowels. It would be quite easy to detect (and exploit) behaviour that decided to use a vowel word just because it somewhat arbitrarily used an 'an'.
Models predict the next word, but they don't just predict the next word. They generate a great deal of internal information in service of that goal. Placing limits on their abilities by assuming the output they express is the sum total of what they have done is a mistake. The output probability is not what it thinks, it is a reduction of what it thinks.
One of Andrej Karpathy's recent videos talked about how researchers showed that models do have an internal sense of not knowing the answer, but fine tuning on question answering I'd not give them the ability to express that knowledge. Finding information the model did and didn't know then fine tuning to say I don't know for cases where it had no information allowed the model to generalise and express "I don't know"