Associate Professor, University of Southern California
2 papers at NeurIPS 2025
We empirically analyze, predict, and mitigate forgetting in upstream data during LLM fine-tuning.
This paper introduces PILS, a novel language model inversion method that leverages the low-dimensionality of next-token distributions, enabling their lossless compression over multiple generation steps for markedly improved prompt recovery.
