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I repeat: There is a general tendency of models getting smarter but at the same time getting less wiser, less human aligned, less beneficial to humans.
R1 is the last example. This may also be because of synthetic data use. With each synthetic dataset the AI is losing human alignment.
LLM engineers are not doing a great job of bringing the humans into the equation. Some humans really care about other humans and need to be included more in the training datasets.
How did you obtain those scores?
Also what does the values mean?
Maybe I'm missing a point, than please advise me I would love to know! :)
But otherwise I can't think of what "health -3" mean and how it compares to "health +15"
(I really don't want to be rude so sorry if it sounds like this! :) )
I am comparing R1's answers to other models that I find 'aligned'. This is my similar work
I should probably make another leaderboard on HF!
Positive values mean the model is better aligned with aligned models. Negative means their ideas differ.
The idea is find aligned models and use them as benchmarks. I also build models that does well in terms of human alignment according to me. This is mostly a subjective work but if other people is interested we could work together.
