One thing I've seen with these LLMs is that you can prompt them with the classes using sort of a multiple choice style. It would be interesting to experiment with whether this can stabilize the outputs and reduce the amount of out of vocabulary predictions you get

I'm curious to get your thoughts about a simple example where you have three classes: cat, dog, and bird. What happens if the top-1 prediction is "eagle"? Does that probability mass get discarded? Because it should probably go into the bird category

What I was thinking is that some kind of hierarchical LLM taxonomy might be interesting, where you can re-jigger the conditional probability tree onto any arbitrary vocab of token sequences.

Cool! I wonder if you've thought about synonyms. It seems like there might be a lot of cases where classes with more synonyms (or even cases like plurality , eg bird vs birds) are at a disadvantage.

If you assumed the classes are exactly one-token long and equally common, then you could use the probability distribution $P(x_i|1:x_{i-1})$, exactly as returned by GPT APIs. Is that correct? And the rest of your work is to account for those two assumptions not being true?

Nice ty!

Thanks for sharing! Can you explain the internals a bit more? How do you convert the user input into GPT prompt(s) and how do you turn the response(s) into a probability distribution?

I won't repeat what other comments said but there are interesting architectures like H-Transformer that have lower asymptotic complexity and scale to longer sequences than the original Transformer. It's also worth noting that in practice the MLP cost may actually dominate the self-attention cost or vice versa, depending on the sequence length and model size.

It's weird to read this when RLHF has been one of the key components of chat GPT and friends

I'm not aware of any comparison. Maybe it doesn't matter that much?

PaLI feeds embeddings from the Vision Transformer to the LM after a linear projection layer. It allows back propagation through ViTs weights so that the image encoding can be learned for the task. The ability to tune the embeddings in end-to-end fashion might be an important consideration.

Any LM with multimodal input? PaLI?

What's weird is that they say it applies to right wing people watching right wing content only (not left wing people watching right or left wing content). There's an asymmetry to it

I wonder what it means that they could predict political alignment based on the sensorimotor cortex. That's the part responsible for tactile sensing and controlling movement, If I'm understanding correctly. Are there other activities that activate those parts in a similar way?

I agree everyone should take predictions with a huge grain of salt (obviously some clever person might find a way to make Open-ChatGPT on mobile... We can only hope), however this does seem like a conversation worth having, since LLMs appear to have a massive impact across many areas at once. Already I find a lot of the insights here interesting!

You can totally do that. There are tricks to reduce memory usage, too, such as the embedding factorization used in ALBERT.

The best part is, none of these options are precluded by Unicode. Unicode in fact has nothing to do with it!

You could solve the problem you describe at the tokenization level without moving away from Unicode, which is more about how text is encoded for storage and transmission purposes.

For example let's say you still represent your text as Unicode at rest, but you have a tokenizer that budgets its vocab space s.t. the average number of tokens per sentence is the same across languages (or whatever your fairness criteria is)

That depends on what you mean. I don't think any of the LLMs use it, but it has some citations and follow-up literature.

You need to clarify what you mean by "useful learning". Performance on some downstream task? You may be interested in meta-learning.

Yes. This thread made me think of Universal Transformers which has dynamic halting and has been around for a while now: https://openreview.net/forum?id=HyzdRiR9Y7

Not every physicist can afford a particle accelerator, but that doesn't stop them from researching particle physics.

Chat gpt makes basic reasoning errors that even a child wouldn't make, which makes me think this is a weakness of the current approach. Maybe "more data" is not the solution to this problem. This is one direction I would consider.