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cs.LG2025
Self-Adapting Language Models
Adam Zweiger, Jyothish Pari, Han Guo +3
Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks, knowledge, or examples. We introduce Self-Adapting LLMs…
cs.LG2025
Log-Linear Attention
Han Guo, Songlin Yang, Tarushii Goel +3
The attention mechanism in Transformers is an important primitive for accurate and scalable sequence modeling. Its quadratic-compute and linear-memory complexity however remain sig…
cs.LG2025
On the Duality between Gradient Transformations and Adapters
Lucas Torroba-Hennigen, Hunter Lang, Han Guo +1
We study memory-efficient optimization of neural networks (in particular language models) with linear gradient transformations, where the gradients are linearly mapped to a lower d…