Comment by xscott
6 hours ago
> A common issue is that it's rarely mentioned on which dataset KL-divergence is computed. It seems the most common dataset is wikitext
Thank you for calling this out. Using Wikipedia snippets for these is a terrible choice. I did a bunch of KL and other stats with the five Gemma 4 models, and the results were non-obvious. Anthropomorphizing:
Gemma 4 31B: "I guess we'll pretend I said this, but it's not me." (Baseline for stats)
Gemma 4 26B: "Dude, I'm certain I wouldn't have said this." (Bad KL)
Gemma 4 12B: "Umm, Me either!" (Similarly Bad KL)
Gemma 4 E4B: "I might say almost anything, this is fine." (Much better KL!!!)
Gemma 4 E2B: "I'm basically a toy. Let's play a game!" (Same KL as E4B)
Anyway, for comparing quantizations, it seems like the largest precision version should be given a one-shot prompt, and the result from that should be used as the corpus for the quantized versions.
> Anyway, for comparing quantizations, it seems like the largest precision version should be given a one-shot prompt, and the result from that should be used as the corpus for the quantized versions.
Way better than wikitext-- but tells you nothing about errors tending to compound or cancel out.
Like say a test shows that only one token in a 10,000 token test would be different. Sounds very close, ship it!-- but what if trajectories with that single different token guarantees failure because it sets in motion a cascade of differences that ultimately result in a final distribution that doesn't include the solution?