Using AI to improve Community Notes

In order to improve the speed at which important community notes get added and to help community noters write better notes, I'm curious if people have put some effort into using a mic of AI (language models) and more simple methods. I'd like to help with this if we can make it work economically.

For example, you could have a scaffolding approach that looks for specific words, which then feeds into an embedding for semantic similarity to contentious issues, and then finally into an LLM that ranks how important the tweet is to have a community and some additional context (through a web search and internal knowledge within the LLM) to help the community noter. I think there's a way to make this economically viable for companies.

Yes, companies, I want Community Notes to expand beyond X. Let's figure out how to connect it to YouTube. Why haven't other social media websites picked it up yet? If they care about truth, this would be a considerable step forward beyond. Notes like “this video is funded by x nation” or “this video talks about health info; go here to learn more” messages are simply not good enough. We need to improve the state of truth-seeking on the internet.

Not just that, as an AI Safety researcher, this is particularly important to me. Don't forget that we train language models on the internet! The more truthful your dataset is, the more truthful the models will be! Let's revamp the internet for truthfulness, and we'll subsequently improve truthfulness in our AI systems!!

No files, tests, or entry points are named. Start by scoping whether the proposed AI/LLM, semantic-similarity, web-search, and YouTube expansion work belongs in this repository; define a concrete first component and acceptance criteria before implementation.