No. If I'm understanding correctly it means the software is learning how to solve problems in general by ingesting examples of procedural problem-solving.
The distinction is that the software doesn't autonomously derive general problem-solving heuristics from scratch. Instead, it observes examples of how humans solve problems procedurally and uses that to replicate similar reasoning. This is crucial because the step-by-step demonstrations give the model structure and guidance, which is different from learning a generalizable strategy for solving any kind of problem without those examples.
In essence, it's like a neural net learning to follow a recipe by watching a chef cook—rather than inventing its own recipes entirely from first principles.
(Current) models may of course lack sufficient training data to act on a metalevel enough ("be creative problem solvers"), or they may lack deep enough representations to efficiently act in a more creative way. (And those two may be more or less the same thing or not.)
This seems consistent with the main points in this paper: one to-the-point statement of the answer to a factual question is all you need [1], while, if you don't have an example of a chain of reasoning in which all of the parameters are the same as those in the prompt, more than one example will be needed.
The authors write "We falsify the hypothesis that the correlations are caused by the fact that the reasoning questions are superficially similar to each other, by using a set of control queries that are also superficially similar but do not require any reasoning and repeating the entire experiment. For the control queries we mostly do not observe a correlation." In the examples of control queries that they give, however, this just amounts to embedding the specific answer to the question asked into language that resembles an example of reasoning to a solution (and in the first example, there is very little of the latter.) The result, in such cases, is that there is much less correlation with genuine examples of reasoning to a solution, but it is not yet clear to me how this fact justifies the claim quoted at the start of this paragraph: if the training set contains the answer stated as a fact, is it surprising that the LLM treats it as such?
[1] One caveat: if the answer to a factual question is widely disputed within the training data, there will likely be many to-the-point statements presented as the one correct answer (or - much less likely, I think - a general agreement that no definitive answer can be given.) The examples given in figure 1 are not like this, however, and it would be interesting to know if the significance of individual documents extends to such cases.
Better observation-based learning is a less expensive way of improving existing corpus-based approaches than trial-and-error and participating in an environment.
Kids seem to have an internal dial for a desired level of perceived danger, and get up to weird stuff, if they don't get enough perceived danger.
If a university professor is giving a lecture on decentralized finance and forks into a recipe for chocolate chip cookies: crack two eggs, add a cup of flour, and fold in brown sugar prior to baking, it would break linearity.
A generalizable strategy for synthesizing LLMs differentiated by their training parameters is a tokenization is isolating data sets and then establishing a lattice in uniformity within the field of technics.
This comment appears to be incoherent and likely AI-generated text. Let me break down why:
1. While it uses technical-sounding terms related to machine learning (LLMs, tokenization, data sets), the way they're strung together doesn't make logical sense.
2. The grammar is incorrect: - "a tokenization is isolating" is not grammatically valid - The sentence structure breaks down in the middle with two "is" statements - The phrase "establishing a lattice in uniformity within the field of technics" is meaningless jargon
3. If we try to interpret what it might be attempting to say about LLMs (Large Language Models), the ideas don't connect in any meaningful way. "Synthesizing LLMs differentiated by their training parameters" could be trying to discuss creating different LLMs with varying parameters, but the rest doesn't follow logically.
4. The term "field of technics" is particularly suspicious - while "technics" is a real word, it's rarely used in AI/ML discussions and seems thrown in to sound technical.
This text shows common hallmarks of AI-generated content that's trying to sound technical but lacks real meaning - it uses domain-specific vocabulary but combines them in ways that don't make semantic sense, similar to how AI models can sometimes generate plausible-looking but ultimately meaningless technical text.
Just like how a chef learns
This is exemplified by how altitude has a meaningful impact but isn’t discussed for a given recipe.
It’s the difference between a painter copying some work and a painter making an original piece and then get feedback on it. We consider the second trial and error because the full process is being tested not just technique.
Imagine if school only gave correct if you used exactly the same words as the book, that is not "trial and error".
Observe implies sentience that, without question, a neural net simply does not possess. "It" certainly 'records', or more specifically it 'maps', but there is no observer in sight (npi).
> mimic
LLM's do not mimic. The magic is mathematical and happening in the high dimensional space. If there is intrinsic underlying pattern and semantic affinities between process X (used in training) and process Y (used in application), it is very likely that both share proximity, possibly form, in some dimensions of the high dimensional model.
There are many ways you could ask a question about how to find the slope of a line, and the “generalization” going on seems to be unifying the different ways you could ask and answer this question.
It seems fair to say that the LLM did learn to find the slope of a line? But the question has definitely been solved before, many times.