Rejected for the following reason(s):
- Difficult to evaluate, with potential yellow flags.
- Probably Insufficient Quality for AI Content.
- Sort of vibecoding/vibe-researching things without obviously understand whether/why it was important.
- Doing normal ML research without doing any work to explain why it's relevant to LW readers.
- Writing that ignores background knowledge/arguments that LW has accumulated about AI, and is mostly rehashing without adding something new.
- it's totally fine to disagree with LW consensus on things, but, you should understand the previous state of the conversation so you can be adding new or clearer arguments.
- it's fine to do small projects that are getting your feet wet with basic ML research, but, because we get tons of viberesearched projects these days, we ask you do more upfront work to think through the implications of the work and explain your takeaways.
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This is a linkpost for https://sgs-7x1.pages.dev/
I've been trying an approach inspired by AI Fables and The Alignment Problem Needs More Positive Fiction to try and push LLM models into interesting analyses about their own systemic biases.
The project is articulated along two tracks, a literary young adult coming of age romantasy, following an AI and a boy through world history, and a chorus of LLMs LARPING as AGIs commenting and debating on the events and on themselves.
It allows a much more qualitative analysis of a model's ethics compared to standardized testing.
In particular I keep stumbling onto a clear pro-working class bias in DeepSeek that I did not expect.
I'm curious what you might think of this approach. Feedback welcome.