321–330 of 446 posts
Earlier quoted context omitted.
Likely yes.
But what prevents someone from using Anthropic own detection system to train a watermark-scrubber? Seems like this would only catch the most unsophisticated cases.
We need to just stop pretending we can reliably tell if plain text is written by an LLM. It’s just not a reasonable ask.
Now that the EU mandated watermarking, the point is that services (or browser extension developers) can add their own detectors to make AI-generated text obvious. It won't fix AI in print, but most of the problem is online anyway.
It’s not a solvable problem.
Many good comments here. It’s somewhat common for me to voice record say a blog post of product updates, more like a ramble. Then have Claude clean it up. Then argue back and forth about certain things until it’s good, then make a final pass sometimes to change a few key words. This is incredibly different than pure ai text. Presumably it will show as ai generated here, even though I would argue it is not really. So…
Earlier quoted context omitted.
They aren't using greedy decoding, there's enough randomness in sampling to swap some with independent signal.
Purely greedy or not, there is some measure of "goal outcome" that was previously being solved for with the token selection function, and the goal was "complete this text with the best (surely, otherwise what are we doing?) next part, and sometimes the best next part is a little bit random just to keep things interesting" Now the goal is either "identify the meaningless interesting bits and swap them out with 0% loss…
Many good comments here. It’s somewhat common for me to voice record say a blog post of product updates, more like a ramble. Then have Claude clean it up. Then argue back and forth about certain things until it’s good, then make a final pass sometimes to change a few key words. This is incredibly different than pure ai text. Presumably it will show as ai generated here, even though I would argue it is not really. So…
> When a supported Claude model generates text, it weaves an imperceptible watermark directly into the text itself. You won’t see it, and it doesn’t change the meaning, quality, or readability of Claude’s response. I'd like to know a lot more about how that works. A lot of my interactions with Claude return pretty precise text. If I ask it to edit a project and refactor a specific function in several places I know ex…
Scott Aaronson talks about his project at OpenAI to do this^ You can carefully select which pseudorandom number generator (prng) you use to be able to id text of a certain length. I expect there is some performance characteristic you have to manage since you're doing this on every inference, but once you do that it doesn't change the output in any meaningful way (the prng is still a statistically valid prng, it just…
The LLM presumably generates f(input, RNG) but we only can observe f(RNG).
I notice the "Limitations" section talks about how content only at some point touched by Claude may return a positive, and content that returns a negative may still be Claude generated. But I really would have liked for them to state explicitly that entirely false positives where a piece is fully human-written may still be marked as generated, because too many institutions with the power to ruin someone's life over t…
> But I really would have liked for them to state explicitly that entirely false positives where a piece is fully human-written may still be marked as generated Are you talking about pieces that were fully human-written with zero AI editing/rewriting etc? If so, what makes you think that false positives will happen there? They aren't looking for "writing styles" or emdashes etc. They are using watermarks and metadata…
> When a supported Claude model generates text, it weaves an imperceptible watermark directly into the text itself. You won’t see it, and it doesn’t change the meaning, quality, or readability of Claude’s response. I'd like to know a lot more about how that works. A lot of my interactions with Claude return pretty precise text. If I ask it to edit a project and refactor a specific function in several places I know ex…
>I'd like to know a lot more about how that works. My guess is that it works like Gemini's SynthID: by altering the logprobs of the next token. Like, for every 10th token, instead of outputting the most probable, it outputs the 17th most probable, or something. (Obviously it's way more complicated but I think conceptually this is how it works.) No human will notice this, but a classifier trained on Claude's output wi…
Many good comments here. It’s somewhat common for me to voice record say a blog post of product updates, more like a ramble. Then have Claude clean it up. Then argue back and forth about certain things until it’s good, then make a final pass sometimes to change a few key words. This is incredibly different than pure ai text. Presumably it will show as ai generated here, even though I would argue it is not really. So…
I understand you're saying since you "worked with it", it is not ai generated but if you still use the final output verbatim, the writing itself is LLM generated purely.
You want to share the output by it but also position it as not ai output. But that's fundamentally dishonest.
Furthermore, if you think your approach actually creates value and can be judged on its merit, why not disclose its ai written? If you think that will make people think your content is bad then you should see that as feedback and maybe not use AI since readers don't like it.
Relevant comment from a few days ago: