Comment by lawlessone
2 years ago
I think this is it.
Generated data is ok if you're curating it to make sure nothing bad, wrong or insensible comes in.
Basically still needs a human in the loop.
2 years ago
I think this is it.
Generated data is ok if you're curating it to make sure nothing bad, wrong or insensible comes in.
Basically still needs a human in the loop.
> Basically still needs a human in the loop.
Yes, and big LLM developers have millions of humans in the loop. That's why they provide free access, for human in the loop filtering & guidance.
If I go to chatGPT and solve a coding task, maybe the first 3 ideas don't work and the 4th works. It can do RLHF setting the first 3 with negative and the fourth with positive score. They just used me to test their model and create a datapoint.
Using LLM is useful both ways - for humans, we get assistance, and LLMs get feedback for their outputs. This seems like the new form of "you are the product".
Then why not remove this crap (LLMs) from the loop altogether? How did we get from "AI will replace you" to "your new job will be an AIs janitor" in the space of about 12 months?
there is nothing wrong with being a janitor. you could also call it "AI editor" instead of you want to insert a job title sounds more prestigious. some people find it easier and more enjoyable to edit a first draft generated by a language model based on instructions than writing that first draft themselves.
I gotta say, Claude is a godsend for building out quick prototypes of ideas, especially when those ideas require domain specific knowledge that you know a little about but aren't specialized in. Which is most interesting programming projects.
Sure, I could do it myself, but it would take more time, each step would have less momentum, and I'd have to think more while I do it. Which, there's a place for that too, of course.
2 replies →
>Then why not remove this crap (LLMs) from the loop altogether
Because reading is faster than writing.
Someone could spend a few years or even most of their life writing a book that can be read in a matter of hours days or weeks.
Humans writing have to proofread their own work. Or occasionally even pay someone else to do it.
No, bad/wrong/nonsense is not the only risk here. You're missing the main point that the authors are making: the shape of the distribution gets changed by this process. A model trained on human data will produce fewer high-perplexity examples than it was trained on (you can see this in Fig 1b, even between generation 0 and 1). In a literal information theory sense, these perplexity values indicate how much information is in each example. Over successive generations models have less actual information to learn from even if they have the same volume of text.
LLMs are milking us of knowledge and skills, repackage them and give it back to us. Models interact with the internet, humans and code execution. They are exploring. Lots of exploring now happens in the chat room, a place where ideas are first tried out. With billions of users, the volume of information LLMs collect from us is huge. We bring references, guidance and feedback right into its mouth, the LLM doesn't even need to do anything like crawling.
Imagine how many things we know, things we accumulated in our life experience, that were never written down anywhere. That information was lost to others. But now we use LLM assistants, so they get to be in the loop and collect tidbits of human life experience that is not written on the internet. And soon they will also work on audio/video and travel with us everywhere, seeing what we show them.
I think that maybe we are too harsh in expecting LLMs to be perfect. If they are based off of human input that is incorrect then we might propagate such errors. But they will still be quicker and much more reliable than most people. Isn’t this good enough? After all, we are willing to accept flaws in people, even including the president. I suspect that the way forward will be to progressively clean the LLM input data as each error gets identified.