Somewhat relatedly: I’m interested on how well LLMs can solve tasks in parallel. This seems very important to me.[1]
The “I’ve thought about this for 2 minutes” version is: Hand an LLM two multiple choice questions with four answer options each. Encode these four answer options into a single token, so that there are 16 possible tokens of which one corresponds to the correct answer to both questions. A correct answer means that the model has solved both tasks in one forward-pass.
(One can of course vary the number of answer options and questions. I can see some difficulties in implementing this idea properly, but would nevertheless be excited if someone took a shot at it.)
- For serial computation the number of layers gives some very rough indication of the strength of one forward-pass, but it’s harder to have intuitions for parallel computation.
- For scheming, the model could reason about “should I still stay undercover”, “what should I do in case I should stay undercover” and “what should I do in case it’s time to attack” in parallel, finally using only one serial step to decide on its action.
There might be more such results I’m currently forgetting about, but they should be relatively easy to find by e.g. following citation trails (to and from the above references) with Google Scholar (or by looking at my recent comments / short forms).
- For scheming, the model could reason about “should I still stay undercover”, “what should I do in case I should stay undercover” and “what should I do in case it’s time to attack” in parallel, finally using only one serial step to decide on its action.
I am also very interested in e.g. how one could operationalize the number of hops of inference of out-of-context reasoning required for various types of scheming, especially scheming in one-forward-pass; and especially in the context of automated AI safety R&D.
“We show that transformers can use meaningless filler tokens (e.g., ‘......’) in place of a chain of thought to solve two hard algorithmic tasks they could not solve when responding without intermediate tokens. However, we find empirically that learning to use filler tokens is difficult and requires specific, dense supervision to converge.”
I looked over it and I should note that “transformers are in TC0” is not very useful statement for prediction of capabilities. Transformers are Turing-complete given rational inputs (see original paper) and them being in TC0 basically means they can implement whatever computation you can implement using boolean circuit for fixed amount of available compute which amounts to “whatever computation is practical to implement”.
I think I remember William Merrill (in a video) pointing out that the rational inputs assumption seems very unrealistic (would require infinite memory); and, from what I remember, https://arxiv.org/abs/2404.15758 and related papers made a different assumption about the number of bits of memory per parameter and per input.
Everything Turing-complete requires infinite memory. When we are saying “x86 set of instructions is Turing-complete” we imply “assuming that processor operates on infinite memory”. It’s in definition of Turing machine to include infinite tape, after all.
It’s hard to pinpoint, but the trick is that it’s very nuanced difference between the sense in which transformers are limited in complexity-theoretic sense and “transformers can’t do X”. Like, there is nothing preventing transformers from playing chess perfectly—they just need to be sufficiently large for this. To answer the question “can transformers do X” you need to ask “how much computing power transformer has” and “can this computing power be shaped by SGD into solution”.
Similarly, I find that GPT-3, GPT-3.5, and Claude 2 don’t benefit from filler tokens. However, GPT-4 (which Tamera didn’t study) shows mixed results with strong improvements on some tasks and no improvement on others.
It’s interesting question whether Gemini has any improvements.
Or maybe not, apparently LLMs are (mostly) not helped by filler tokens.
Somewhat relatedly: I’m interested on how well LLMs can solve tasks in parallel. This seems very important to me.[1]
The “I’ve thought about this for 2 minutes” version is: Hand an LLM two multiple choice questions with four answer options each. Encode these four answer options into a single token, so that there are 16 possible tokens of which one corresponds to the correct answer to both questions. A correct answer means that the model has solved both tasks in one forward-pass.
(One can of course vary the number of answer options and questions. I can see some difficulties in implementing this idea properly, but would nevertheless be excited if someone took a shot at it.)
Two quick reasons:
- For serial computation the number of layers gives some very rough indication of the strength of one forward-pass, but it’s harder to have intuitions for parallel computation.
- For scheming, the model could reason about “should I still stay undercover”, “what should I do in case I should stay undercover” and “what should I do in case it’s time to attack” in parallel, finally using only one serial step to decide on its action.
I would expect, generally, solving tasks in parallel to be fundamentally hard in one-forward pass for pretty much all current SOTA architectures (especially Transformers and modern RNNs like MAMBA). See e.g. this comment of mine; and other related works like https://twitter.com/bohang_zhang/status/1664695084875501579, https://twitter.com/bohang_zhang/status/1664695108447399937 (video presentation), Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks, RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval.
There might be more such results I’m currently forgetting about, but they should be relatively easy to find by e.g. following citation trails (to and from the above references) with Google Scholar (or by looking at my recent comments / short forms).
I am also very interested in e.g. how one could operationalize the number of hops of inference of out-of-context reasoning required for various types of scheming, especially scheming in one-forward-pass; and especially in the context of automated AI safety R&D.
https://arxiv.org/abs/2404.15758
“We show that transformers can use meaningless filler tokens (e.g., ‘......’) in place of a chain of thought to solve two hard algorithmic tasks they could not solve when responding without intermediate tokens. However, we find empirically that learning to use filler tokens is difficult and requires specific, dense supervision to converge.”
Thanks, seen it; see also the exchanges in the thread here: https://twitter.com/jacob_pfau/status/1784446572002230703.
I looked over it and I should note that “transformers are in TC0” is not very useful statement for prediction of capabilities. Transformers are Turing-complete given rational inputs (see original paper) and them being in TC0 basically means they can implement whatever computation you can implement using boolean circuit for fixed amount of available compute which amounts to “whatever computation is practical to implement”.
I think I remember William Merrill (in a video) pointing out that the rational inputs assumption seems very unrealistic (would require infinite memory); and, from what I remember, https://arxiv.org/abs/2404.15758 and related papers made a different assumption about the number of bits of memory per parameter and per input.
Also, TC0 is very much limited, see e.g. this presentation.
Everything Turing-complete requires infinite memory. When we are saying “x86 set of instructions is Turing-complete” we imply “assuming that processor operates on infinite memory”. It’s in definition of Turing machine to include infinite tape, after all.
It’s hard to pinpoint, but the trick is that it’s very nuanced difference between the sense in which transformers are limited in complexity-theoretic sense and “transformers can’t do X”. Like, there is nothing preventing transformers from playing chess perfectly—they just need to be sufficiently large for this. To answer the question “can transformers do X” you need to ask “how much computing power transformer has” and “can this computing power be shaped by SGD into solution”.
It’s interesting question whether Gemini has any improvements.