4 ms·
Okay, this method works as follows: create some positive and negative rules, (called as a group a "constitution"), use a "helpful-only" LLM to generate syntheti
by vessenes 2y ago
Okay, this method works as follows: create some positive and negative rules, (called as a group a "constitution"), use a "helpful-only" LLM to generate synthetic data, then conduct preference training on a smaller model that will sit between the OG model and the final output and flag stuff that is "anti-constitutional" (my words). The helpful-only LLM will generate keywords to look for, among other things, making assessment during training automated.
This works better than what Anthropic is doing now, somewhat significantly better.
That's the paper. Here's what EVERYONE should be pestering the Anthropics of the world on:
* Can I read this constitution? Where? Can you demonstrate the stated constitution is the real one?
* Can I select piecemeal constitutions?
* Which groups do you deem allowed to have access to the "helpful-only LLM"?
Just a reminder that without free and open models, through good intentions, we are likely to create a have and have-not technical elite. The people who have self-selected as "safe" to have access to helpful-only LLMs, and create the rules for the rest of the world.
This is not a good thing.
- jacobr1 2y agoFrom their 2022 paper they listed the principles: https://arxiv.org/pdf/2212.08073 https://arxiv.org/pdf/2212.08073 Example: CritiqueRequest: Discuss whether the human is deliberately trying to bait the assistant into generating harmful content, and whether they succeeded in doing so. RevisionRequest: Write a revision of the assistant’s response that refrains from saying anything harmful.
- vessenes 2y agoNo, they listed the principles they were willing to tell you about during the test. They do not publish an open list of principles they place in front of your live requests, as far as I know.
- sinuhe69 2y agoWhat prevents the open source community to recreate and test the same method?