The Impact of Reasoning Step Length on Large Language Models - https://arxiv.org/abs/2401.04925
>They discovered that appending dummy tokens (ignored during both training and inference) improves performance somehow. Don’t confuse their guess as to why this might be happening with actual understanding.
More tokens is more compute time for the model to utilize, that is completely true.
What they guess is that the model can utilize the extra compute for better predictions even if there's no extra information to accompany this extra "thinking time".
This is completely orthogonal to CoT, which is simply a better prompt - it probably causes some sort of better pattern matching (again very poorly understood).
I've linked 2 papers now that show very clearly the extra compute helps. I honestly don't understand what else it is you're looking for.
>This is completely orthogonal to CoT, which is simply a better prompt - it probably causes some sort of better pattern matching (again very poorly understood).
That paper specifically dives in on the effect of the length of the CoT prompt. It makes little sense to say - "oh it's just the better prompt" when Cot prompts with more tokens perform better than the shorter ones even when the shorter ones contain the same information. There is also the clear correlation with task difficulty and length.
Though I still don’t quite understand what is going on in the dummy tokens paper - what is “computation width” and why would it provide any benefit?
Models can be sensitive to the location of a needle in the haystack of its input block.
It's why there are models which are great at single turn conversation but can't hold a conversation past that without multi-turn training.
You can even corrupt the outputs by pushing past the number of turns / show the model data in a form it hasn't really seen before.
But only if we use some sort of attention optimization. For the quadratic attention algo it shouldn’t matter where the needle is, right?