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Earlier quoted context omitted.
> In short: LLM have no concept, or even desire to produce of truth They do produce true statements most of the time, though.
That's just because true statements are more likely to occur in their training corpus.
Try to explain why one shotting works.
Super useful exercise. My gut tells me that someone will soon figure out how to build micro-LLMs for specialized tasks that have real-world value, and then training LLMs won’t just be for billion dollar companies. Imagine, for example, a hyper-focused model for a specific programming framework (e.g. Laravel, Django, NextJS) trained only on open-source repositories and documentation and carefully optimized with a spec…
It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. It really is the antithesis to the human brain, where it rewards specific knowledge
This is the entire breakthrough of deep learning on which the last two decades of productive AI research is based. Massive amounts of data are needed to generalize and prevent over-fitting. GP is suggesting an entirely new research paradigm will win out - as if researchers have not yet thought of "use less data".
> It really is the antithesis to the human brain, where it rewards specific knowledge
No, its completely analogous. The human brain has vast amounts of pre-training before it starts to learn knowledge specific to any kind of career or discipline, and this fact to me intuitively suggests why GP is baked: You cannot learn general concepts such as the english language, reasoning, computing, network communication, programming, relational data from a tiny dataset consisting only of code and documentation for one open-source framework and language.
It is all built on a massive tower of other concepts that must be understood first, including ones much more basic than the examples I mentioned but that are practically invisible to us because they have always been present as far back as our first memories can reach.
I'm half shocked this wasn't on HN before? Haha I built PicoGPT as a minified fork with And it's small enough to run from a QR code :) https://kuber.studio/picogpt/ You can quite literally train a micro LLM from your phone's browser
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Earlier quoted context omitted.
It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. It really is the antithesis to the human brain, where it rewards specific knowledge
> It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. This is the entire breakthrough of deep learning on which the last two decades of productive AI research is based. Massive amounts of data are needed to generalize and prevent over-fitting. GP is suggesting an entirely new research paradigm will win out - as if researchers have not yet thought of "use less data". >…
> What’s the deal with “hallucinations”? The model generates tokens by sampling from a probability distribution. It has no concept of truth, it only knows what sequences are statistically plausible given the training data. Extremely naiive question.. but could LLM output be tagged with some kind of confidence score? Like if I'm asking an LLM some question does it have an internal metric for how confident it is in its…
Yes, the actual LLM returns a probability distribution, which gets sampled to produce output tokens. [Edit: but to be clear, for a pretrained model this probability means "what's my estimate of the conditional probability of this token occurring in the pretraining dataset?", not "how likely is this statement to be true?" And for a post-trained model, the probability really has no simple interpretation other than "thi…
Basically, you’d need a lot more computing power to come up with a distribution of the output of an LLM than to come up with a single answer.
Super useful exercise. My gut tells me that someone will soon figure out how to build micro-LLMs for specialized tasks that have real-world value, and then training LLMs won’t just be for billion dollar companies. Imagine, for example, a hyper-focused model for a specific programming framework (e.g. Laravel, Django, NextJS) trained only on open-source repositories and documentation and carefully optimized with a spec…
This is possible but not for training but fine-tuning the existing open source models. This can be mainstream, and then custom model fine-tuning becomes the new “software development”. Please check out this new fine-tuning method for LLM by MIT and ETH Zurich teams that used a single NVIDIA H200 GPU [1], [2], [3]. Full fine-tuning of the entire model’s parameters were performed based on the Hugging Face TRL library.…
The typos are interesting ("vocavulary", "inmput") - One of the godfathers of LLMs clearly does not use an LLM to improve his writing, and he doesn't even bother to use a simple spell checker.
vocabulary*
*In the code above, we collect all unique characters across the datasetEarlier quoted context omitted.
> It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. This is the entire breakthrough of deep learning on which the last two decades of productive AI research is based. Massive amounts of data are needed to generalize and prevent over-fitting. GP is suggesting an entirely new research paradigm will win out - as if researchers have not yet thought of "use less data". >…
There is actually a whole lot of research around the "use less data" called data pruning. The goal in a lot of cases there is basically to achieve the same performance with less data. For example [1] received quite some attention in the past. [1] https://arxiv.org/abs/2206.14486
That will not change the fact that a coding model has to learn vastly many foundational capabilities that will not be present in such a dataset as small as all the python code ever written. It will mean much less python than all the python ever written will be needed, but many other things needed too in representative quantities.
Can you train this on say Wikipedia and have it generate semi-sensible responses?
First no is that the model as is has too few parameters for that. You could train it on the wikipedia but it wouldn’t do much of any good.
But what if you increase the number of parameters? Then you get to the second layer of “no”. The code as is is too naive to train a realistic size LLM for that task in realistic timeframes. As is it would be too slow.
But what if you increase the number of parameters and improve the performance of the code? I would argue that would by that point not be “this” but something entirely different. But even then the answer is still no. If you run that new code with increased parameters and improved efficiencly and train it on wikipedia you would still not get a model which “generate semi-sensible responses”. For the simple reason that the code as is only does the pre-training. Without the RLHF step the model would not be “responding”. It would just be completing the document. So for example if you ask it “How long is a bus?” it wouldn’t know it is supposed to answer your question. What exactly happens is kinda up to randomness. It might output a wikipedia like text about transportation, or it might output a list of questions similar to yours, or it might output broken markup garbage. Quite simply without this finishing step the base model doesn’t know that it is supposed to answer your question and it is supposed to follow your instructions. That is why this last step is called “instruction tuning” sometimes. Because it teaches the model to follow instructions.
But if you would increase the parameter count, improve the efficiency, train it on wikipedia, then do the instruction tuning (wich involves curating a database of instruction - response pairs) then yes. After that it would generate semi-sensible responses. But as you can see it would take quite a lot more work and would stretch the definition of “this”.
It is a bit like asking if my car could compete in formula-1. The answer is yes, but first we need to replace all parts of it with different parts, and also add a few new parts. To the point where you might question if it is the same car at all.