scottmu

11 ms·

1.
by scottmu 6mo ago
If 2 (or more) tokens are synonymous with each other with high probabilities (49.9% each for a total of 99.8%), that's still low entropy. Not as low as a singular high-probability token, but low enough for us to consider this a low-ent
2.
by scottmu 6mo ago
I like the direction you're going with this strategy. There are many approaches, nuances, edge cases, and clever tricks to each of these steps, even without taking into account token probability distributions. Very powerful to get it r
3.
by scottmu 6mo ago
Yes, there's a wide variety of use cases that require different ratios of accuracy/speed. If you require 3 responses to be accurate, you have to multiply all 3 response accuracy probabilities, and as you've shown, this can re
4.
by scottmu 6mo ago
Great question. What I can say is we experimented a _ton_. If you take a basic approach and simply ask the same prompt of a bunch of LLMs and then ask another LLM to combine the results, you'll get a pretty poor answer. At best, you&#x
5.
by scottmu 6mo ago
You're right! I could've phrased my comment better. Ken actually wanted to edit his post, but it was too late. So he asked me to write a response explaining what he meant. Of course, he could've commented too. I was just tryi
6.
by scottmu 6mo ago
I wouldn't say it's easy to detect hallucinations. Understanding output token probability distributions is only part of a solution, and we still aren't perfect. Just better than individual models. Hallucinations may seem rare
7.
by scottmu 6mo ago
I've felt your pain. Models aren't always trained well enough on edge cases and configs. Would love to hear how Sup works out for you.
8.
by scottmu 6mo ago
I want to clarify what Ken meant by "entropy in the output token probability distributions." Whenever an LLM outputs a token, it's choosing that token out of all possible tokens. Every possible output token has a probability