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Oh wow, so it worked pretty well on data it hasn't seen. That expected but cool to reproduce. Have you seen this leaderboard of sorts[1], and this proposal to
by adamgordonbell 2mo ago
Oh wow, so it worked pretty well on data it hasn't seen. That expected but cool to reproduce.
Have you seen this leaderboard of sorts[1], and this proposal to change hutter prize[2]?
I think it's a really clever idea that you could measure an LLM's prediction abilities and language understanding by some sort of held-out compression metric because file sizes are very concrete. They are already beating shannon's numbers using a human prediction for compression, from what i can see.
https://github.com/hkust-nlp/llm-compression-intelligence https://github.com/hkust-nlp/llm-compression-intelligence
https://gwern.net/hutter-prize https://gwern.net/hutter-prize
- krackers 1mo ago>I think it's a really clever idea that you could measure an LLM's prediction abilities and language understanding by some sort of held-out compression metric This is the premise of https://huggingface.co/spaces/Jellyfish042/UncheatableEval https://huggingface.co/spaces/Jellyfish042/UncheatableEval
- adamgordonbell 1mo agovery cool
- andai 1mo agoNice. This ranking basically matches other benchmarks, from what I can tell. Which implies this would probably also hold for the larger models, which are sadly not included in the leaderboard.
- krackers 1mo agoI think it's limited to using base models (non post-trained), because the post-training would skew the logit distribution. There are ways to "coax" post-trained models back into behaving "like" a base model, I wonder if the benchmark could be unofficially updated with those somehow.