J. Rosser

Scaling Influence Functions in LLMs through Eigenbasis-Corrected One-Bit Gradient Projection

This work describes the optimal fixed-dimensional linear representation and proposes eigenbasis-corrected one-bit gradient projection (EOGP) to approximate it at scale and predicts retraining outcomes more accurately than the evaluated compression baselines while using one-sixteenth of their per-example storage.