Dissecting learning and forgetting in language model finetuning
2024-01-16 · ICLR 2024 poster · anchor
Findings
- IC-1518Domain finetuning of LLaMA 2 7B, LLaMA 2 13B, and GPT-2 XL on PubMed causes topic and style priors to shift dramatically, accounting for the majority of the probability change, while factual knowledge learning contributes only a small fraction
- IC-1519Topic and style biases in LLaMA 2 7B are learned like simple features (rapidly, with minimal capacity, concentrated at the first few tokens, magnified by learning rate) while factual knowledge is learned like complex features (slowly, requiring significant capacity, uniformly across positions, unaffected by learning rate)