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Explore how Large Language Models learn and process structured data in this comprehensive lecture delivered at the International Centre for Theoretical Sciences. Delve into the theoretical foundations underlying LLM capabilities for handling structured information, examining the mechanisms that enable these models to understand and generate organized data patterns. Learn about the probabilistic and optimization principles that govern how neural language models internalize structural relationships within datasets. Discover the mathematical frameworks and computational approaches that allow LLMs to extract meaningful patterns from structured inputs, bridging the gap between raw data organization and model comprehension. Gain insights into the theoretical underpinnings of modern language model architectures and their capacity for structured reasoning, presented as part of the Data Science: Probabilistic and Optimization Methods program focusing on core principles enabling current successes and future breakthroughs in machine learning.
