Maybe some H-Test tasks can be affected by this. But how do you explain tasks like Repeated Word (one group has two repeated words) or End Punctuation (based on the location of the punctuation).
I don’t think I need to. ‘End Punctuation’ sounds like it’s affected by tokenization, and regardless, artificial microbenchmarks like ‘Repeated Word’ are not expected to exhibit smooth scaling the way global losses like perplexity do. (They instead exhibit emergence, inverse U-scaling, and noisy patterns due to combined sampling error & biases from model checkpoints / sizes / test items / test sizes / prompts+formatting.) Look at Big-Bench to see how noisy these sorts of things are even when they are being properly evaluated in controlled conditions and sweeping model sizes (whereas your results are an uninterpretable hodge-podge).
Meanwhile, how do you explain the PaLM results on spelling miracles if you don’t believe in scaling and that these are tasks “language models don’t learn”?
We tested 15 models from leading LLM labs before we arrived at our claim. If the H-Test was a “scaling task”, we would have observed at least some degree of performance improvement in other models like Luminous or LLaMA too. But no this was not the case.
We see improvements from scaling all the time which start from a flatline and then increase at critical sizes. See ‘emergence’. Emergence is not that surprising because phase transitions are everywhere in NNs; and obviously, people don’t bother with creating benchmarks where all the LLMs are ~100%, and then the best model, GPT-4, has a chance to exhibit emergence. And, doubtless, we’ll see more examples with GPT-5 etc. (You also have a higher opinion of some of these ‘leading’ models like Luminous than I think most people do.)
Our section 5 (Analysis: We Don’t Understand GPT-4) is in fact dedicated to disproving the claim that more orthography-specific data will help LLMs solve H-Test. In GPT-3.5-Turbo finetuning results on H-Test training set, we observed no significant improvement in performance. Before and after finetuning, the performance remains tightly centered around the random change baseline.
Why would finetuning on a training set help a test set if GPT-3.5 is memorizing? Memorizing a pair of rhymes A/B tells you nothing about another pair of rhymes C/D, regardless of the two tasks being ‘in-domain’.
(By the way, I would be skeptical of any conclusions drawn from GPT-3.5 finetuning because even if the ‘finetuning’ seemed to work, who knows what that ‘finetuning’ mystery meat actually is? The first iteration of OA’s GPT-3 finetuning was apparently a fiasco, somehow whenever the rebooted OA GPT-3 finetuning comes up the result from it always seems to be ‘it doesn’t help capabilities’, and OA declines to explain in any detail what the ‘finetuning’ does.)
To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.
I don’t think I need to. ‘End Punctuation’ sounds like it’s affected by tokenization, and regardless, artificial microbenchmarks like ‘Repeated Word’ are not expected to exhibit smooth scaling the way global losses like perplexity do. (They instead exhibit emergence, inverse U-scaling, and noisy patterns due to combined sampling error & biases from model checkpoints / sizes / test items / test sizes / prompts+formatting.) Look at Big-Bench to see how noisy these sorts of things are even when they are being properly evaluated in controlled conditions and sweeping model sizes (whereas your results are an uninterpretable hodge-podge).
Meanwhile, how do you explain the PaLM results on spelling miracles if you don’t believe in scaling and that these are tasks “language models don’t learn”?
We see improvements from scaling all the time which start from a flatline and then increase at critical sizes. See ‘emergence’. Emergence is not that surprising because phase transitions are everywhere in NNs; and obviously, people don’t bother with creating benchmarks where all the LLMs are ~100%, and then the best model, GPT-4, has a chance to exhibit emergence. And, doubtless, we’ll see more examples with GPT-5 etc. (You also have a higher opinion of some of these ‘leading’ models like Luminous than I think most people do.)
Why would finetuning on a training set help a test set if GPT-3.5 is memorizing? Memorizing a pair of rhymes A/B tells you nothing about another pair of rhymes C/D, regardless of the two tasks being ‘in-domain’.
(By the way, I would be skeptical of any conclusions drawn from GPT-3.5 finetuning because even if the ‘finetuning’ seemed to work, who knows what that ‘finetuning’ mystery meat actually is? The first iteration of OA’s GPT-3 finetuning was apparently a fiasco, somehow whenever the rebooted OA GPT-3 finetuning comes up the result from it always seems to be ‘it doesn’t help capabilities’, and OA declines to explain in any detail what the ‘finetuning’ does.)
Thanks for the comment. I’ll get back to you sometime soon.
Before I come up with anything though, where are you getting to with your arguments? It would help me draft a better reply if I knew your ultimatum.
Where am I going? Nowhere complex.