People are just as bad as my LLMs
wilsoniumite.com
People are just as bad as my LLMs
1–10 of 173 posts
Re: People are just as bad as my LLMs
#2No they are not randomly wrong or right without perspective unless they have some kind of brain injury. So that's against the title but the rest of their point is interesting!
Re: People are just as bad as my LLMs
#3There has been some good research published on this topic of how RLHF, ie aligning to human preferences easily introduces mode collapse and bias into models. For example, with a prompt like: "Choose a random number", the base pretrained model can give relatively random answers, but after fine tuning to produce responses humans like, they become very biased towards responding with numbers like "7" or "42".
Re: People are just as bad as my LLMs
#4The "person one" vs "person two" bias seems trivially solvable by running each pair evaluation twice with each possible labelling and the averaging the scores.
Although of course that behavior may be a signal that the model is sort of guessing randomly rather than actually producing a signal.
Re: People are just as bad as my LLMs
#5interleaving a bunch of people's comments and then asking the LLM to sort them out and rank them…seems like a poor method. The whole premise seems silly, actually. I don't think there's any lesson to draw here other than that you need to understand the problem domain in order to get good results from an LLM.
Re: People are just as bad as my LLMs
#6[deleted]
Re: People are just as bad as my LLMs
#7[deleted]
I've had very similar experiences.
How quickly and easily people are willing to give up first class sources is quite frightening
Re: People are just as bad as my LLMs
#8There has been some good research published on this topic of how RLHF, ie aligning to human preferences easily introduces mode collapse and bias into models. For example, with a prompt like: "Choose a random number", the base pretrained model can give relatively random answers, but after fine tuning to produce responses humans like, they become very biased towards responding with numbers like "7" or "42".
Why is that? Whenever I’m giving examples I almost always use 7, something ending in a 7 or something in the 70s
Re: People are just as bad as my LLMs
#9Very nice article. But the title, and the idea, is the very frequent "racist" form of the proper "People [can be] just as bad as my LLMs".
Now: some people can't count. Some people hum between words. Some people set fire to national monuments. Reply: "Yes we knew", and "No, it's not necessary".
And: if people could lift the tons, we would not have invented cranes.
Very, very often in these pages I meet people repeating "how bad people are". That is "how bad people can be", and "and we would have guessed these pages are especially visited by engineers, who must be already aware of the importance of technical boosts" - so, besides the point relevant to the fact that the median does not represent the whole set, the other point relevant to the fact that tools are not measured on reaching mediocre results.
Re: People are just as bad as my LLMs
#10There has been some good research published on this topic of how RLHF, ie aligning to human preferences easily introduces mode collapse and bias into models. For example, with a prompt like: "Choose a random number", the base pretrained model can give relatively random answers, but after fine tuning to produce responses humans like, they become very biased towards responding with numbers like "7" or "42".
Why is that ? Whenever I’m giving examples I almost always use 7, something ending in a 7 or something in the 70s
Veritasium actually made a video on this concept about a year ago: https://www.youtube.com/watch?v=d6iQrh2TK98