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Sholto Douglas & Trenton Bricken - How to Build & Understand GPT-7's Mind
Dwarkesh Podcast
Transfer learning: FT on math problems helps models do entity recognition
Fine-tuning models on math problems leads to improved entity recognition capabilities. Research shows that fine-tuning with math problems enhances attention to positions of different elements, aiding in coding and manipulating math equations. Evidence suggests that training models on code enhances reasoning and language skills. Modeling code implies modeling a challenging reasoning process used in its creation, which can potentially transfer to other types of reasoning problems. The significance lies in the models' ability to comprehend reasoning processes beyond mere word predictions; there is evidence that models engage in actual reasoning. Interpretability techniques reveal models' generalization abilities, including learning from game sequences and influential data points. Even small transformers can be explicitly encoded to perform basic reasoning processes, indicating that models can learn these processes medically.Overall, the models are under-parameterized, learning to flow gradients and acquiring more general skills.
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