Human feedback improves AI-generated Community Notes but participation limits their adoption rate, with collaborative notes serving a complementary role to human and AI-only notes.
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ST-MoE introduces stability techniques for sparse expert models, allowing a 269B-parameter model to achieve state-of-the-art transfer learning results across reasoning, summarization, and QA tasks at the compute cost of a 32B dense model.
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How Human Feedback Shapes AI-generated Community Notes
Human feedback improves AI-generated Community Notes but participation limits their adoption rate, with collaborative notes serving a complementary role to human and AI-only notes.
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ST-MoE: Designing Stable and Transferable Sparse Expert Models
ST-MoE introduces stability techniques for sparse expert models, allowing a 269B-parameter model to achieve state-of-the-art transfer learning results across reasoning, summarization, and QA tasks at the compute cost of a 32B dense model.