An Introduction to In-Context Learning

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• Juyeon Heo, John Bronskill, University of Cambridge
• Wednesday 19 March 2025, 11:00-12:30
• Cambridge University Engineering Department, CBL Seminar room BE4-38..

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In-context learning (ICL) has emerged as a powerful capability of large language models (LLMs), allowing them to adapt to new tasks without explicit parameter updates. This talk begins with an introduction to meta-learning and neural processes, which lay the foundation for ICL . We then move on to transformer based ICL where the model can be trained from scratch or leverage pre-trained LLMs. To attempt to understand why ICL works, we discuss its connections to Bayesian inference, kernel regression, and gradient descent. Finally, we examine potential safety concerns in ICL , highlighting risks and challenges in reliable AI deployment.

This talk is part of the Machine Learning Reading Group @ CUED series.

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