3 ms·

I'm guessing - probably some textual variation on Benford's law? [1]. Trivial for compute, painful for a human. - "Ensure distribution of vowels is in >99th pe

by toufka 2mo ago

I'm guessing - probably some textual variation on Benford's law? [1]. Trivial for compute, painful for a human. - "Ensure distribution of vowels is in >99th percentile of human work" - "Ensure the distribution of the letter "s" is within 99th percentile of human work" - "Ensure the distribution of the letter "L" is periodic with periodicity within 5% of 1/N characters. - "Ensure there is a cross-linguistic 'typo' (colour vs color) at 1/N words, where N: 1000 = Model1, 2000 = Model2, 3000 = Model3. - "Ensure the distribution of tense error is within 99th percentile of human work" If more than 3 dimensions have a score >99% percentile of human, let's call it watermarked... - 1) https://en.wikipedia.org/wiki/Benford%27s_law https://en.wikipedia.org/wiki/Benford%27s_law

Computer0 2mo ago
I would hate to have any of these rules effecting my output
- limbicsystem 2mo ago
  I see what you did there!
Der_Einzige 2mo ago
Models can't reliably follow instructions involving their own logprobs unless they can take agentic control and use quite sophisticated dynamic grammars/structures/constraints to force this behavior in one shot (which can be slow and the dynamic grammar modification feature isn't supported in closed model APIs for safety reasons) or repeated attempts at rewriting which is expensive/slow. Yes they can do this, but it's more likely closer to the original "red token, green token" paper: https://arxiv.org/abs/2301.10226 https://arxiv.org/abs/2301.10226 i.e. take half of your LLMs vocabulary, and upweight its probabilities by ~55% to the other half's ~45%, and scan for overuse of this half of all tokens. You can even choose a different half/slice for every individual user, for every individual action. You can implement this under the hood cheaply with logit-biasing.
- Terretta 2mo ago
  Considering how weirdly detuned tokens selections have become in Anthropic's LLM prose in recent models, there is a chance this goes unnoticed in everyday use.
- lovetocode 2mo ago
  Well put. I wonder if this will inevitably increase usage costs because the model has to do more work to find a solution that meets watermarking standards.

by toufka 2mo ago

Computer0 2mo ago

I would hate to have any of these rules effecting my output

limbicsystem 2mo ago
I see what you did there!

Der_Einzige 2mo ago

Models can't reliably follow instructions involving their own logprobs unless they can take agentic control and use quite sophisticated dynamic grammars/structures/constraints to force this behavior in one shot (which can be slow and the dynamic grammar modification feature isn't supported in closed model APIs for safety reasons) or repeated attempts at rewriting which is expensive/slow. Yes they can do this, but it's more likely closer to the original "red token, green token" paper: https://arxiv.org/abs/2301.10226 https://arxiv.org/abs/2301.10226 i.e. take half of your LLMs vocabulary, and upweight its probabilities by ~55% to the other half's ~45%, and scan for overuse of this half of all tokens. You can even choose a different half/slice for every individual user, for every individual action. You can implement this under the hood cheaply with logit-biasing.

Terretta 2mo ago
Considering how weirdly detuned tokens selections have become in Anthropic's LLM prose in recent models, there is a chance this goes unnoticed in everyday use.
lovetocode 2mo ago
Well put. I wonder if this will inevitably increase usage costs because the model has to do more work to find a solution that meets watermarking standards.