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Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns
Vatsal Baherwani, Zixi Chen, Shikai Qiu +2
Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabilities such as in-context learn…
DynaGuard: A Dynamic Guardian Model With User-Defined Policies
Monte Hoover, Vatsal Baherwani, Neel Jain +7
Guardian models play a crucial role in ensuring the safety and ethical behavior of user-facing AI applications by enforcing guardrails and detecting harmful content. While standard…
Dense Backpropagation Improves Training for Sparse Mixture-of-Experts
Ashwinee Panda, Vatsal Baherwani, Zain Sarwar +4
Mixture of Experts (MoE) pretraining is more scalable than dense Transformer pretraining, because MoEs learn to route inputs to a sparse set of their feedforward parameters. Howeve…