81–90 of 183 posts
Hi! Author here. Surprised to see this on HN now. Happy to answer any questions! Some context about this: - This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs - Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but use…
Earlier quoted context omitted.
Kind of hijacking, would you say that LLM's have solved the frame problem? To me, the frame problem is: Can you function in an open vs closed world, and to me the answer is yes, LLM's can definitely function in an open world where the rules are fuzzy, changing, undefined, etc. At the very least, much better than all GOFAI approaches by far. The issue is now grounding - It can "function", but what would it take to "gr…
One of these is a much weaker claim than the other. > yes, LLM's can definitely function in an open world where the rules are fuzzy, changing, undefined, etc. > At the very least, much better than all GOFAI approaches by far
I do think the first claim has real merit even if it's not 100% on par with humans. Second claim is just true.
Hi! Author here. Surprised to see this on HN now. Happy to answer any questions! Some context about this: - This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs - Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but use…
Hi! Author here. Surprised to see this on HN now. Happy to answer any questions! Some context about this: - This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs - Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but use…
>> NOT an LLM. its a small ar transforme Super cool project! Though, aren't most modern LLM's ar transformers internally?
Earlier quoted context omitted.
Basically, you have a bunch of Q,A pairs in the training dataset. Here, it was trained to next-word predict the question itself, as well as next-word predict the answer given the question as prompt. This is bog-standard, no one's complaining. In the test dataset's Q,A pairs, it was only trained to next-word predict the question itself, and it was not given the answer at all. It was then evaluated by seeing if it is a…
Further question—the model produces an answer to the question, it sends the answer, and then gets graded. Does it get to know immediately how it did, or does it get the grade back at the end after answering all the questions? If it is the former case, it would be possible to add the generated question/answer pair into the training set as well. Would that be considered fair? (Of course this is a moot point if the answ…
Hi! Author here. Surprised to see this on HN now. Happy to answer any questions! Some context about this: - This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs - Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but use…
Hi! Author here. Surprised to see this on HN now. Happy to answer any questions! Some context about this: - This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs - Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but use…
Also, in curating the training data in a deliberate manner, with attention to detail. Most people just use existing datasets and call it a day. It's a lot of work, which is why there are gains on the table.
I don’t have any kind of ML background but I have always thought of sample efficiency as the great unsolved problem of AI. We humans have unbelievably good sample efficiency; often we can durably learn something on just a single example or two. This is the main area in which LLMs are vastly, vastly behind us.
I think(?) you’ve already probably done a good job of explaining this criticism for semi-informed people. But can you dumb it down even more for those of us who are almost entirely out-of-the-loop? > Training on the eval puzzles is cheating / “training on test” > No this is false. “Training on test” specifically means training on the labels of test data. The labels were not trained on. > Also, ARC is a metalearning b…
The point of ARC is essentially an "IQ Test" for AI systems. It is meant to cover abstract reasoning capabilities of generally-intelligent systems like LLMs. What the author did here was build a system that only solves ARC problems. The other tension is the fact that this score is on the public eval set. In machine learning, you typically have 3 datasets: training, evaluation, and test. The training set is the datase…