This paper is heavily optimizing to solve ARC specifically. Generally applicable ideas are not directly described.
However people smarter than me may benefit from the ideas described here.
You need to elaborate on this idea. The way forward for what?
Highly optimized AI is how we've *always* done things. LLMs with very general abilities are an extremely new phenomenon, but they still aren't reliable enough to take the place of specialized AI.
Look back in the thread. They were talking about ChatGPT 3.5 which was definitively and exclusively a model that communicated in language tokens. Not video. Not audio. Not spacial. Just language tokens.
Yes, of course AGI will have NLP capabilities, but it will probably learn those the same way humans do, by observing language in context, not by being fed a billion books and doing fill-in-the blank games. We only play those games because we don't know of an efficient and bio-realistic way to train the models.
u/Mysterious-Rent7233 I am curious what you think about this model:
[https://arxiv.org/html/2408.11039v1](https://arxiv.org/html/2408.11039v1)
I believe this is the way to ASI, what are your thoughts?
Whatever happens in other threads stays in those threads. I m just curious about your statement. What does "AGI will not be a language model" even means. A machine learning language model by definition is a machine learning model that can process natural languages, so if it has NLP capabilities, it is a language model, it is NOT JUST a language model.
Wildly incorrect. In no sense are decoder-only LMs "a narrow AI specialized for language production"
There are essentially zero language specific architectural features in language models. Arguably the choice of tokenization scheme, but if you remove that, it's literally just a sequence model. It even works on images (non 1D data serialized to 1D) just fine, as seen in the original DALL-E paper.
LLMs are not chatboats. They are much, much more general than the other models you mentioned.
GPT 3.5 100% used a BPE tokenizer. Also, calling GPT3 "essentially useless" is only meaningful relative to more recent techniques. You might need to (re?)-read the paper to remind yourself of the historical context. https://arxiv.org/abs/2005.14165
More importantly, your contributions to this discussion are extremely unproductive. Take a breath. Drink some water. Maybe reconsider if engaging here is worth your emotional energy.
>GPT 3.5 100% used a BPE tokenizer.
A BPE tokenizer is a tokenizer that is specialized to match its dataset, which for GPT 3.5 was language (and with a bias towards western languages).
For a transformer to shed its bias towards language, it would use bits or bytes as its tokens:
[https://arxiv.org/abs/2105.13626](https://arxiv.org/abs/2105.13626)
[https://arxiv.org/html/2406.19223v1](https://arxiv.org/html/2406.19223v1)
[https://arxiv.org/abs/2401.13660](https://arxiv.org/abs/2401.13660)
> Also, calling GPT3 "essentially useless" is only meaningful relative to more recent techniques. You might need to (re?)-read the paper to remind yourself of the historical context. [https://arxiv.org/abs/2005.14165](https://arxiv.org/abs/2005.14165)
If that before accusing me of being unhelpful, you should read what I actually write and spend the effort to try to understand what I am saying. First you confused byte-level tokenization with BPE so you didn't understand the relationship to multi-modal, which was the context of the conversation.
Now you are making the same mistake, claiming that I said that GPT 3.5 was useless. I said that it was _essentially_ useful for NON-LINGUISTIC tasks. Which is born out by the very first sentence of the link you sent me:
> Recent work has demonstrated substantial gains on many NLP tasks and benchmarks.
The L is NLP is "Language", meaning "Linguistic". GPT 3.5 was a huge breakthrough in linguistic processing and was essentially useless for anything else.
If you remember the historical context then you'll remember what a huge deal it was when GPT-4 was shown to have some very rudimentary spatial abilities.
https://www.linkedin.com/pulse/pink-unicorn-chatgpt-4-proves-can-think-bryan-brownlie-dq1ye/
GPT-3 and 3.5 could not do that, because they were excellent linguistic models and nothing more.
It remains the case to this day that GPT 3.5 is useless for tasks that require spatial, aural, ... awareness. This does not seem to me to be a controverials hot take but simply common sense.
I read what both of you have to say and u/Mysterious-Rent7233 is correct, the thing about most people is they actually do not take time to read and they postulate based on incorrect data, Props to you u/Mysterious-Rent7233 for staying calm and explaining correctly +1
General AI using specialized AIs. The general one needs base levels high enough to orchestrate, understand, review, plan and iterate on results given by specialized AIs, doesn't need to be on par with the specialized ones. Different objectives, different tasks.
Or have the models adapt at inference time. I think the key is working out how to:
a. Efficiently train models / model routers during inference (i.e. test time training)
b. Build expert model weights that don't degrade the quality of the overall model. MoE feels like a like a crude step in this direction.
I feel that people massively underestimate degradation of LLMs through "heavy optimization". Truly, ML algos in last couple decades were single-purpose/area oriented. But what makes LLMs what they are is broadness of their dataset.
As long as your dataset is clean, it seems to scale with ability to reason correctly in any area. Its particularly easy to notice that LLMs acquire ability to produce grammatically and synthetically correct text in any language before they learn reasoning for even just 1 language.
Right now approach seems to be essentially a condensation of larger LLM into smaller one, which is why they perform better than previous generation, trained directly from impure dataset. But unless you can train specialized "L for Large" model, you wont be able to guarantee quality of condensed dataset. So we're still tied to Larger models.
>LLMs acquire ability to produce grammatically and synthetically correct text in any language before they learn reasoning for even just 1 language.
This is also a very important distinction when comparing to how we, humans learn. For us, it's the opposite - we learn the very basic reasoning from the environment before we learn to speak. For example, a child playing with toys would soon learn which items could not be put into a small box. So later, when the child learns to speak, they will never mess up the fact that something does not fit somewhere. Even if you gave a child a book that has stories about horses living in pockets, the child would immediately know that it's fiction and the world does not work that way. An LLM, on the other hand, might easily come up with "And then I took a horse out of my pocket to ride to the castle."
For LLMs, the priority seems to be defined by the number of occurrences and not by the actual ground truth about real-world facts. So, training with highly distilled data seems to be the best we can do for now. But how much of this distilled data do we need to counterweight all the possible wrong conclusions that an LLM might hallucinate?
For a more general "intelligence" we need better architectures, so that the data could be prioritized by some kind of a ground-truth regarding basic logic and science. Only after getting a solid core, we can throw a ton of text at it and be sure that it doesn't get confused.
"Narrow AI" has always existed. It's just not nearly as economically valuable as integrated AI. Who wants to pay $100M to train an AI for just one thing? And who wants to pay millions to software developers (like me) to integrate all of these narrow AIs? And who wants to deal with the issues when the narrow AIs miscommunicate the way we see daily when ChatGPT and DALL-E cannot correct images to the user's preferences because one doesn't understand languages well enough and the other doesn't understand images well enough.
Narrow AI is only "the way forward" if we fail to build what the market really wants: AGI.
No it's not.
We programmers have been doing this from the start.
We need a general system that can optimise for all tasks and we will get there, when these systems are embodied.
The big problem is it's using a language model that was likely contaminated on answers to the public dataset, so in some ways they may be coaxing that data out.
Yeah that sounds about right, glad it seems to be what I guessed, the bitter lesson as in we need to create some form of active infrence for true intelligence to emerge
The real magic of neural networks doesn’t occur in the passive infrence, it occurs in the training stage
As long as the model can dynamically modify itself while in use then we’re on our way to something really cool
But it’s not a completely dead path either. A lot of problem spaces that models underperform are peppered with analogous examples with solutions! LLMs themselves make it easier to retrieve them from a corpus.
I haven’t tried this myself and the paper doesn’t seem to cover it outside of some titles in graphs —- but is the test time compute method for this narrow use case a lot better than just taking a bunch of examples and injecting them into the prompt? That’s the typical way to make models seem smarter for, admittedly easier stuff, in industry for stuff like plain text to SQL.
Yeah, things like RAG are a lot better for tasks that require knowledge or are very similar to existing problems with known solutions. The ARC challenge is a little harder to use RAG-like techniques on, since the given tasks have different reasoning patterns that are required and reliably detecting those is one of the hardest parts.
The test-time finetuning technique is specifically for useful for finding these patterns, where they don't take outside examples from a corpus, but instead augment the examples that are given in the specific task (adding rotations, color permutations, etc) and then finetune on that.
In language tasks, finetuning is something that is already done a lot. The only real difference here is that we do augmentation from a very small amount of samples and finetune on that to learn a given task.
Seems like maybe it is a good idea then if we have real problems that fit and have (or could have) adjacent exhales we can retrieve at inference time. Especially for weaker models that are so much cheaper to run.
LLM has context ... so after filling up a context during conversation by learning new things the model should go into a sleep mode.
Then filter "useful" data and structure in a proper way for assimilation.
After that a new acquired data should be used to retrain a model weights.
Such a process should be done every time if we have interaction with LLM.
Such process should be done with bigger models like 30b or 70b+ because are enough big for finding better patterns and connections in data.
But that needs absurd of compute power.
Yes just add sleep to LLMs. I recommend reading up on how LLMs work at a deeper level and see how your ideas relate to actual implementation. It is really not as simple as just blindly feeding its context in as a dataset.
I know it's not a simple .
Such a filled context must be done way filtered and properly structured and also need probably extra examples inside new data ,etc .
..and retaining the model each time with a new data is heavy for current technology... absurd compute demand each time.
But I think the idea is proper.
We also have context which is heavily filtered during a day and new data is assimilated to a long term memory at night during a sleep and we wake up with a clean context ready to operate 😅.
See the paper here: [https://ekinakyurek.github.io/papers/ttt.pdf](https://ekinakyurek.github.io/papers/ttt.pdf)
I don't yet see their results on [https://arcprize.org/](https://arcprize.org/), so hopefully the arcprize team can validate it (and run it on their semi-private eval to make sure it hasn't overfit.
It will not show up on the private leaderboard because they validated on public eval set and not on the private set for the competition. They couldn't validate with the private since they submission to the competition requires <12hrs completion time and their model.
Since they use the public eval set, it could also mean that the model they used has been already exposed to the data in training before. They also described that in the limitations section of this paper.
Isn't TTT basically cheating though? I thought the TTT paper was like a shitpost or an April Fools joke or something like that. Maybe I'm thinking of something else.
I think the key difference is if you are training on the answers or not. I think it's fair game to train as much as you want on the questions. The paper you are thinking of is: Pretraining on the Test Set Is All You Need
but isn't ARC as a benchmark sort of like this:
- example, input -> output
- example, input -> output
- example, input -> output
- input -> now write the output
It's literally giving you some ground truth examples to train on, so yes it's basically training on the test set.
Isn't that how humans would take the test as well though? I've done about 100 of the hard evaluation set (they are fun puzzles) and after 20-30 of them I found it got a lot easier to do the rest because I learned all the tricks to them.
It might be even more innocuous than this. What you're describing is something like learning on all previous test set questions to answer new test set questions better (as a way to get more training data). My understanding is that the insight of TTT comes from learning on the current question independently of all other test set questions (as a way to fix distribution mismatches between the current question and all training questions). See section 2.3 of the paper "Thus, TTT trains a specialized prediction model for each test input, obtained by fine-tuning a base model on a test-time dataset generated from that test input."
That is, after coming up with some model M, for each test question i, we fine-tune M on the question statement (which includes (input, output) examples for just that question) to get M\_i. For example, when we go from question 1 to question 2 of the test set, we again restart with the original model M so M\_2 = train(M, Q2) instead of M\_2 = train(M\_1, Q2). Hopefully that makes sense.
Did you learn those tricks just because you saw the example inputs? Or did you learn those tricks because you saw the correct answer to those examples and that helped you learn how this worked?
My understanding here is that TTT is essentially a kind of in-context learning. You're conditioning your network on the features relevant to the input example. I think a better analogy to human test taking would be entering a "flow state" from focusing on a particular problem for a long time, such that all of your thoughts become "painted" by features of the problem at hand.
It's training on the examples, not memorizing on the answer key. Training on examples is exactly what a human or AGI should do. Memorizing the answer key is of course cheating.
You're probably thinking of "Pretraining on the Test Set Is All You Need" (https://arxiv.org/abs/2309.08632), but as far as I understand it, TTT does not actually train on the answers. ARC is set up so you get a small sample of (input, output) pairs before having to predict a hidden output for one more input. The earlier examples can be used (along with various forms of augmentation) to form a simple training set that the model can be fine-tuned on before figuring out the last output.
"Data Leakage: Even though the base Llama-3 perform extremely poorly on the public validation set, the
public availability of the dataset on various platforms (GitHub, Kaggle) introduces the possibility that these
models may have encountered these examples during pre-training."
The real deal ARC-AGI dataset is private. I'm not convinced this is anything but happy to be proven wrong.
It's finetuning on the problem examples and augmentations of them without having access to the answer.
ARC gives you a few examples with completions so that you can then solve the completion for a final problem example from what you observed in the example completions.
They take the examples and act as if the examples are tests, which they then train on. In this benchmark you have n examples and k tests. They don't touch the tests during the finetuning, but simply look at the n examples and now for each of the n examples, take the other n-1 example input/output pairs and finetune the model to predict the output example from the left-out input.
Test-time fine tuning (also leveraged by MindsAI) is a way to do on-the-fly recombination of the vector functions contained in a DL model to adapt to a new task.
Here is Ekin's X Thread going into the details of the methodology
https://x.com/akyurekekin/status/1855680785715478546?s=46&t=tMxZqJeuhNmuh3e0D8XHYw
test-time training is also how alphaproof beat imo--probably will be next paradigm at least it needs a lot of compute for bitter lesson sense it's good
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