Comments URL: https://news.ycombinator.com/item?id=46969632

Points: 2

# Comments: 0

Do you have any benchmarks of min-p sampling with the new reasoning models, such as QwQ and R1?

Benchmarks: https://github.com/ggerganov/llama.cpp/issues/11474#issuecom...

Intuively speaking, most people think of writing as a communication tool. But actually it's also a thinking tool that helps create deeper connections over discrete thoughts which can only occupy a fixed slice of our attention at any given time. Attentional capacity the primary limitation-- for humans and LLMs. So use the token space as extended working memory. Besides, even the Coconut paper got mediocre results. I don't think this is the way.

This creates a gap between the mechanical measurement of certainty and true understanding, much like mistaking the map for the territory or confusing the finger pointing at the moon with the moon itself.

I've done some work before in this space, trying to come up with different useful measures from the logprobs, such as measuring shannon entropy over a sliding window, or even bzip compression ratio as a proxy for information density. But I didn't find anything semantically useful or reliable to exploit.

The best approach I found was just multiple choice questions. "Does X entail Y? Please output [A] True or [B] False. Then measure the linprobs of the next token, which should be `[A` (90%) or `[B` (10%). Then we might make a statement like: The LLM thinks there is a 90% probability that X entails Y.