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Self-Adapting Language Models

arxiv.org

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Re: Self-Adapting Language Models

#51

Post by robviren »

gavinray wrote: post #45

Two close friends of mine who were math prodigies that went on to do ML very early (mid 2010's) were always talking to me about an algorithm that sounds similar to this: "NEAT/HyperNEAT" (Neuroevolution of Augmented Topologies) [0] I'm no ML practictioner, but as I understood it, the primary difference between NEAT and what is described in this paper is that while NEAT evolves the topology of the network, this paper…

I just got sucked into this idea recently! After some success with using genetic algorithms to clone voices for Kokoro I wondered if it would be possible to evolve architecturers. So interested in the idea of self assembled intelligence, but do wonder how it can be made feasible. A hybrid approach like this might be for the best given how llms have turned out.

Re: Self-Adapting Language Models

#55

Post by perrygeo »

> Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks

The learning and inference process are entirely separate, which is very confusing to people familiar with traditional notions of human intelligence. For humans, learning things and applying that knowledge in the real world is one integrated feedback process. Not so with LLMs, we train them, deploy them, and discard them for a new model that has "learned" slightly more. For an LLM, inference is the end of learning.

Probably the biggest misconception out there about AI. If you think LLMs are learning, it's easy to fantasize that AGI is right around the corner.

Re: Self-Adapting Language Models

#56

Post by johnsmith1840 »

khalic wrote: post #49
johnsmith1840 wrote: post #31

Earlier quoted context omitted.

AGI likely a combination of these two papers + something new likely along the lines of distillation. 1. Preventing collapse -> model gets "full" https://arxiv.org/pdf/1612.00796 2. Forgetting causes better generalization https://arxiv.org/abs/2307.01163 3. Unknow paper that connects this - allow a "forgetting" model that improves generalization over time. - I tried for a long time to make this but it's a bit difficul…

There is no sign that LLMs are capable of general reasoning, on the contrary, so hold your horses about that. We have proven they can do basic composition (as a developer, I see proof of this every time I generate some code with an assistant) which is amazing already, but we’re still far from anything like “general intelligence”.

My argument is that we already have psuedo/static reasoners. CL will turn our non reasoners into reasoners.

CL has been an open problem from the very beginnings of AI research with basically no solution. Its pervasiveness indicates a very deep misunderstanding on our knowledge of reasoning.

Re: Self-Adapting Language Models

#57

Post by kovek »

perrygeo wrote: post #55

> Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks The learning and inference process are entirely separate, which is very confusing to people familiar with traditional notions of human intelligence. For humans, learning things and applying that knowledge in the real world is one integrated feedback process. Not so with LLMs, we train them, dep…

What if you can check if the user responds positively/negatively to the output, and then you train the LLM on the input it got and the output it produced?

Re: Self-Adapting Language Models

#59

Post by fspeech »

perrygeo wrote: post #55

> Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks The learning and inference process are entirely separate, which is very confusing to people familiar with traditional notions of human intelligence. For humans, learning things and applying that knowledge in the real world is one integrated feedback process. Not so with LLMs, we train them, dep…

Reinforcement learning can be used to refine LLM as shown by Deepseek.

Re: Self-Adapting Language Models

#60

Post by perrygeo »

fspeech wrote: post #59
perrygeo wrote: post #55

> Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks The learning and inference process are entirely separate, which is very confusing to people familiar with traditional notions of human intelligence. For humans, learning things and applying that knowledge in the real world is one integrated feedback process. Not so with LLMs, we train them, dep…

Reinforcement learning can be used to refine LLM as shown by Deepseek.

Everything I've read in the last 5 months says otherwise. Probably best described by the Apple ML group's paper call The Illusion of Thinking. It empirically works, but the explanation could just be that making the stochastic parrot squawk longer yields a better response.

In any case, this is a far cry from what I was discussing. At best, this shows an ability for LLMs to "learn" within the context window, which should already be somewhat obvious (that's what the attention mechanism does). There is no global knowledge base or weight updates. Not until the content gets published, rescraped, and trained into the next version. This does demonstrate a learning feedback loop, albeit one that takes months or years, driven by external forces - the company that trains it. But it's way too slow to be considered intelligent, and it can't learn on its own without help.

A system that truly learned, ie incorporated empirical data from its environment into its model of the world, would need to do this in millisecond time frames. Single celled organisms can do this. Where you at AGI?

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