Dwarkesh Podcast cover image

Sholto Douglas & Trenton Bricken - How to Build & Understand GPT-7's Mind

Dwarkesh Podcast

NOTE

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.

00:00
Transcript
Play full episode

Remember Everything You Learn from Podcasts

Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
App store bannerPlay store banner