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I recall reading recently that someone went back and trained an RNN at a similar scale to a GPT and got similar performance on modern hardware (perhaps someone
by mjburgess 2y ago
I recall reading recently that someone went back and trained an RNN at a similar scale to a GPT and got similar performance on modern hardware (perhaps someone can link me that paper?).
ie., the innovation in statistical AI isn't in making the algorithms "smarter", it's finding ways to align the computation with modern GPU hardware -- this has been the story since 2012.
In the end, the function all such algs are approximating is a conditional probability. ie., the perfect answer to any prompt is to ignore training entirely, and at inference time, compute an expectation across all historical data. All training does is essentially optimally cache a large part of that computation.
This is very different to how it's typically sold/understood, in the sense that there's an appearance that at inference-time some unbounded computation is going on, ie., "thinking"/"reasoning"/etc. But at inference time for any prompt the same amount of computation is used, regardless of the question complexity. So the system will appear to reason (etc.) if it can sample convincingly from its pre-cached computation.
This means "innovation" here follows a moore's law S-curve for GPU hardware.
- doctorpangloss 2y agoBut often, less training performs better.
- mjburgess 2y agoYou'd have to give me an example. If less training performs better, it's because it's overfitting. In the case of NLP/CV you, in general, cannot really "overfit" because most of the goal isnt predictive, but rather representational -- so training=templating, rather than predicting. In these cases training for longer should mostly improve the quality of the output, since there isnt any ground truth to overfit against.
- deleted 2y ago[deleted]
- kasmura 2y agoI disagree. Ring attention and tree attention are so general that the core ideas are independent of details about modern GPUs. Maybe so for flash attention but not this. I also disagree because these algorithms are fundamentally about enabling long context by distributing across gpus and this would not be enabled by “moored law for gpu hardware”
- viraptor 2y ago> But at inference time for any prompt the same amount of computation is used, regardless of the question complexity. That's not true and that's why the "think step by step" works. The output becomes part of the context. Forcing that way of answering essentially merges two queries into one: split question into subtasks and solve each subtask separately. The complex question does cause more computation and provides better quality answers.
- mjburgess 2y agoSure, it's not quite true, but it's close enough for the analysis to be correct. ie., if we take a family of problems: P1..n each statable with prompts P1..n, each having an 'essential complexity to solve' O(P1)...O(Pn) then it's trivial to show that no curve-fitting statistical algorithm has the 'correct' computational budgets associated to each. The budget is basically constant, and is not increasing correctly as the problem complexity increases. (Eg., consider the prompt: by applying Dijkstra's algorithm, find a path....on graph G1, G2..Gn -- with ever more complex graphs). It is right to say that step-by-step prompting expands the computational resources available to the LLM and therefore often improves the quality of the answer.. but it is incorrect to say that it does so by allocating computation according to that complexity. At best you could say that it is we, the human prompter, seeing the answer is bad, are increasing its budget -- so we are partially modelling the complexity of the problem for the LLM. If you could create an LLM that "prompted itself" according to the complexity of the problem, and measured its energy/time/etc. to be as we would expect.. then you'd be on firmer grounds with this claim. But i'd be inclined to deny this was possible given what an LLM is... ie., I claim that I can always find a family of problems P1..Pn where the computational distribution will rule out that reasoning is taking place. Why? Because the LLM is only sampling from a compression of training data, I deny it is at all sensitive to the meaning of the terms. So whereas a person will understand a novel problem (family) on the basis of its meaning, and expend time appropriately, the LLM is limited to sampling from prior examples of such problems in training data.
- viraptor 2y ago> ie., I claim that I can always find a family of problems P1..Pn where the computational distribution will rule out that reasoning is taking place. Finding edge cases is not a great way to evaluate probabilistic processes. You'll almost always find a pathological case. I'm sure we can find a series of questions of increasing difficulty where humans think they're of decreasing difficulty. That wouldn't prove humans don't reason. > If you could create an LLM that "prompted itself" according to the complexity of the problem You don't have to create one. We know that in-context learning is roughly equivalent to training, so you can add instructions about evaluating task difficulty and different approaches depending on the result to the system prompt. You'll see it's possible to do that estimate. Agents use that all the time to decide when to split a task into subtasks and when to proceed with solving. > So whereas a person will understand a novel problem (family) on the basis of its meaning, and expend time appropriately, the LLM is limited to sampling from prior examples of such problems in training data. Can you prove humans aren't just sampling from training data + preserving context? I don't believe anyone has really documented how human thought works at that level. I'm not saying it's true or false, just - do we actually know enough to validate the claim you made?
- FeepingCreature 2y agoRather, the amount of computation for every token is bounded.
- spencerchubb 2y agoHopefully people will figure out how to make AI work with dynamic compute rather than fixed compute!