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In-context learning (ICL) in large language models (LLMs) utilizes input-output examples to adapt to new tasks without altering the underlying model architecture. This method has transformed how models handle various tasks by learning from direct examples provided during inference. The problem at hand is the limitation of a few-shot ICL in handling intricate tasks.
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Microsoft has decided to offer AI-on-the-cheap for businesses willing to settle for a little less than cutting-edge. Specifically, the tech titan is peddling three new AI engines — from a new family of AI offerings dubbed Phi-3 — that are significantly less powerful than say ChatGPT-4 Turbo. Even so, they often still get the job done. Observes lead writer Karen Weise: “The smallest Phi-3 model can fit on a smartphone , so it can be used even if it’s not connected to the Interne
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Created Using Ideogram Next Week in The Sequence: Edge 391: Our series about autonomous agents continues with the fascinating topic of function calling. We explore UCBerkeley’s research on LLMCompiler for function calling and we review the PhiData framework for building agents. Edge 392: We dive into RAFT, UC Berkeley’s technique for improving RAG scenarios.
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