LLMs and Model Bias
Large models can outperform frontier models with minimal training and data, highlighting the importance of architectural inductive biases. A unique approach involves stripping an LLM of its language capabilities to focus solely on numerical outputs, significantly reducing computational demands. This raises intriguing questions about the nature of intelligence in models, whether they exhibit general intelligence or remain specialized in familiar tasks.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Daniel Franzen & Jan Disselhoff - ARC Prize 2024 winners
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