2 papers
cs.LG2026
Influence-Preserving Proxies for Gradient-Based Data Selection in LLM Fine-tuning
Sirui Chen, Yunzhe Qi, Mengting Ai +4
Supervised fine-tuning (SFT) relies critically on selecting training data that most benefits a model's downstream performance. Gradient-based data selection methods such as TracIn…
cs.CL2025
Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
Tianci Liu, Ruirui Li, Yunzhe Qi +8
Large language models (LLMs) have achieved remarkable performance on various natural language tasks. However, they are trained on static corpora and their knowledge can become outd…