maybe it would work if they could encourage end users to be rigorous? (ie, detect if they have the capability to rate well and then reward them when they do by comparing them against other highly rated raters of the same phenotype)
> Voilà: bold text, emojis, and plenty of sycophancy – every trick in the LMArena playbook! – to avoid answering the question it was asked.
This is hard to swallow.
I don't believe a single word this article says. Apparently the "real author" (the human being who wrote the original prompt to generate this article) only intend to use this article to generate clicks and engagement but don't care at all about what's in there.
The general conceit of this article, which is something that many frontier labs seem to be beginning to realize, is that the average human is no longer smart enough to provide sufficient signal to improve AI models.
Aside from Meta is there any reason to think the big AI labs are still using LMArena data for training? The weaknesses are well understood and with the shift to RL there are so many better ways to design a reward function.
> They're not reading carefully. They're not fact-checking, or even trying.
It’s not how I do, and I suppose how many people do. I specifically ask questions related to niche subjects that I know perfectly well and that is very easy for me to spot mistakes.
The first time I used it, that’s what came naturally to my mind. I believe it’s the same for others.
When they released GPT-4.5, it was miles ahead of others when it comes to its linguistic skills and insight. Yet, it was never at top of the arena - it felt that not everone was able to appreciate the edge.
True and what you can realize/read between the lines is something deeper.
LLMs are fallible.
Humans are fallible.
LLMs improve (and improve fast).
Humans do not (overall, ie. "group of N experts in X", "N random internet people").
All those "turing tests" will start flipping.
Today it's "N random internet humans" score too low on those benchmarks, tomorrow it'll be "group of N expert humans in X" score too low.
Is there a reason wrong data isn't considered more broadly in its context as still valuable?
Shouldn't the model effectively 1. learn to complete the incorrect thing and 2. learn the context that it's correct and incorrect? In this case the context being lazy LMArena users. And presumably, in the future, poorly filtered training data.
We seem to be able to read incorrect things and not be corrupted (well, theoretically). It's not ideal, but it seems an important component to intellectual resilience.
It seems like the model knowing the data is LMArena, or some type of un-trusted, would be sufficient to shift the prior to a reasonable place.
this argument is also broadly true about the quality and correctness of posts on any vote-based discussion board
> Why is LMArena so easy to game? The answer is structural.
> The system is fully open to the Internet. LMArena is built on unpaid labor from uncontrolled volunteers.
also all user's votes count equally, bu not all users have equal knowledge.
>Would you trust a medical system measured by: which doctor would the average Internet user vote for?
Yes, the system desperately needs this. Many doctors malpractice for DECADES.
I would absolutely seek to, damn, even pay good money to, be able to talk with a doctor's previous patients, particularly if they're going to perform a life-changing procedure on me.
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[ 3.3 ms ] story [ 53.5 ms ] threadThis is hard to swallow.
I don't believe a single word this article says. Apparently the "real author" (the human being who wrote the original prompt to generate this article) only intend to use this article to generate clicks and engagement but don't care at all about what's in there.
It’s not how I do, and I suppose how many people do. I specifically ask questions related to niche subjects that I know perfectly well and that is very easy for me to spot mistakes.
The first time I used it, that’s what came naturally to my mind. I believe it’s the same for others.
Meta "cheated" on lmarena not by using a smarter model but by using one that was more verbose and friendly with excessive emojis.
LLMs are fallible. Humans are fallible. LLMs improve (and improve fast). Humans do not (overall, ie. "group of N experts in X", "N random internet people").
All those "turing tests" will start flipping.
Today it's "N random internet humans" score too low on those benchmarks, tomorrow it'll be "group of N expert humans in X" score too low.
Shouldn't the model effectively 1. learn to complete the incorrect thing and 2. learn the context that it's correct and incorrect? In this case the context being lazy LMArena users. And presumably, in the future, poorly filtered training data.
We seem to be able to read incorrect things and not be corrupted (well, theoretically). It's not ideal, but it seems an important component to intellectual resilience.
It seems like the model knowing the data is LMArena, or some type of un-trusted, would be sufficient to shift the prior to a reasonable place.
This is pure gold. I've always found this approach of evals on a moving-target via consensus broken.
> Why is LMArena so easy to game? The answer is structural. > The system is fully open to the Internet. LMArena is built on unpaid labor from uncontrolled volunteers.
also all user's votes count equally, bu not all users have equal knowledge.
They've raised about $250 million, so I don't see that happening anytime soon.
Maybe if they started ranking the answers on a 1-10 range, allowing people to specify graduations of correctness/wrongness, then the crowd would work?
https://en.wikipedia.org/wiki/The_Wisdom_of_Crowds
Yes, the system desperately needs this. Many doctors malpractice for DECADES.
I would absolutely seek to, damn, even pay good money to, be able to talk with a doctor's previous patients, particularly if they're going to perform a life-changing procedure on me.