One hypothesis for how transformers generate text is that they calculate semantically meaningful primitives in early layers of the residual stream, which are converted to a high-level execution plan in middle layers, followed by concrete tokens in the final layers.

Is there any empirical evidence for this? Or is this just a general observation?

Regarding the visual instruction tuning paper, see (https://arxiv.org/pdf/2402.11349.pdf, Table 5). Though this experiment on multi-modality was rather simple, I think it does show that it's not a convenient way to improve on H-Test.

Out of genuine curiosity, can you link to your sources?

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.

I also want to point you to this (https://arxiv.org/abs/2402.11349, Appendix I, Figure 7, Last Page, "Blueberry?: From Reddit u/AwkwardIllustrator47, r/mkbhd: Was listening to the podcast. Can anyone explain why Chat GPT doesn’t know if R is in the word Blueberry?"). Large model failures on these task types were rather a widely observed phenomenon but with no empirical investigation.

About 1.) Agree with this duality argument.

About 2.) I'm aware of the type of tasks that suddenly increase in performance at a certain scale, but it is rather challenging to confirm assertions about the emergence of capabilities at certain model scales. If I made a claim like "it seems that emergence happens at 1TB model size like GPT-4", it would be misleading as there are too many compound variables in play. However, it would also be a false belief to claim that absolutely nothing happens at such an astronomical model size.

Our paper's stance, phrased carefully (and hopefully firmly), is that larger models from the same family (e.g., LLaMA 2 13B to LLaMA 2 70B) don't automatically lead to better H-Test performance.  In terms of understanding GPT-4 performance (Analysis: We Don’t Understand GPT-4), we agreed that we should be blunt about why GPT-4 is performing so well due to too many compound variables.

As for Claude, we refrained from speculating about scale since we didn't observe its impact directly. Given the lack of transparency about model sizes from AI labs, and considering other models in our study that performed on par with Claude on benchmarks like MMLU, we can't attribute Claude's 60% accuracy solely to scale. Even if we view this accuracy as more than marginal improvement, it suggests that Claude is doing something distinct, resulting in a greater boost on H-Test compared to what one might expect from scaling effects on other benchmarks.

About 3.) Fine-tuning can indeed be effective for prompting models to memorize information. In our study, this approach served as a useful proxy for testing the models' ability to learn from orthography-specific data, without yielding substantial performance improvements on H-Test.

I appreciate this analysis. I'll take more time to look into this and then get back to write a better reply.

So to summarize your claim (check if I'm understanding correctly):

1. Character-level tokenization can lead to different results.
- My answer: Yes and No. But mostly no. H-Test is not just any set of character manipulation tasks.
- Explanation: 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). Though this opinion is valid and is probably worthy of further investigation, it doesn't disprove the full extent of our tests. Along similar lines, GPT-4 shows some of the most "jumping" performance improvement from GPT 3.5 in non-character-level tasks (Repeated Word: 0.505 -> 0.98). 

2. Scaling up will lead to better results. Since no other models tested were at the scale of GPT-4, that's why they couldn't solve H-Test.
- My answer: No but it would be interesting if this turned out to be true.
- Explanation: 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. And the research that you linked doesn't seem to devise a text-to-text setup to test this ability.

3. Memorization (aka more orthography-specific data) will lead to better results.
- My answer: No.
- Explanation: 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.

"image caption generation and video simulation currently used in Sora will partially correct some of these errors." I'm in line with this idea.

I initially thought so until the GPT-4 results came back. "This is an inevitable tokenizer-level deficiency problem" approach doesn't trivially explain GPT-4's performance near 80% accuracy in Table 6 <https://arxiv.org/pdf/2402.11349.pdf, page 12>. Whereas most others stay at random chance.

If one model does solve these tasks, it would likely mean that these tasks can be solved despite the tokenization-based LM approach. I just don't understand how.