Comment by deepwoods
15 hours ago
I wonder how well this would translate with a much larger model. They ran the experiments with comparatively tiny models. You would presumably need to churn a lot more tokens to move the needle of a multi-trillion-parameter model's weights. It might just be that it's not that hard to improve Llama-3B and Llama 3-8B because they're not very good to begin with. They compare it to other methods - in-context learning and TTT - but I'd be curious to see it compared to a "control group" of RLHF.
As an aside, I get that this is a preprint, but it's very odd to see a piece of writing with obvious typos and grammar issues (e.g. page 13: "We attributes this results to," "In this setting (see Table 4), Sleep achieves a 80% success rate, higher then the other methods.") in an era with LLMs. It sort of locks in that this was written by human beings, and I'm all for that, but it jumps out at me more than it used to.
I'm seeing so many more typos on the internet than I did a few years ago. I'm convinced some of it is deliberate to ensure readers the writer isn't AI.
I think you meant "assure readers", my evidently human fellow!
From my own experience, a significant minority of my daily writing is sent to an LLM, which is very forgiving of typos. I would not be surprised if people are getting sloppier.
I have a theory that every typo you make distracts the LLM from whatever you are trying to get it to do, so I avoid typos as much as possible. I have no evidence that this is true though.
Yes we are.
in my case it very much is. im now deliberately leaving slightly off from correct writing, or exaggerated slightly. as a sort of organic behavioral kintsugi
The funny thing is that you can easily get a LLM to introduce typos and grammar mistakes if you ask it.