
Omar Shaikh
@oshaikh13 • 2,097 subscribers
member of sociotechnical staff @Stanford
Videos

What’s the point of a “helpful assistant” if you have to always tell it what to do next? In a new paper, we introduce a reasoning model that predicts what you’ll do next over long contexts (LongNAP 💤). We trained it on 1,800 hours of computer use from 20 users. 🧵
Omar Shaikh123,679 görüntüleme • 3 ay önce

We upgraded Tabracadabra 🎉 to bring an entire context-aware assistant (not just tab to autocomplete!) to any textbox. It's pretty great if you hate switching between the chat interface and what you're working on. We're also open-sourcing, so you can try it out!🧵
Omar Shaikh40,762 görüntüleme • 1 ay önce

LLMs sound homogeneous *because* feedback modalities like rankings, principles, and pairs cater to group-level preferences. Asking an individual to rank ~1K outputs or provide accurate principles takes effort. What if we relied on a few demos to elicit annotator preferences?
Omar Shaikh52,304 görüntüleme • 2 yıl önce
Daha fazla içerik yok.