MechSparse: Mechanism-Guided Sparse PEFT Selection Is Task-Shaped
Mechanistic interpretability identifies sparse subsets of heads and MLP blocks that carry specific behaviors. We ask whether such causal signals can guide where to place a small PEFT budget more effectively than the cheap heuristics practitioners already use. \method{} scores attention heads and MLP blocks by normalized activation-patching recovery on clean/corrupted probes and trains LoRA/QLoRA only on the selected sites; \methodc{} adds bounded credit for small within-layer joint subsets. We compare against random, magnitude, activation-norm, and gradient/Fisher on Ministral-8B/NF4 in three cells: Swahili span-JSON information extraction (IE) at $b{=}0.25\%$ and $1.0\%$, and English$\to...
| Authors | Son Ha Xuan, Phat T. Tran-Truong, Xuan-Bach Le |
|---|---|
| Primary category | cs.CL |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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