2 papers
cs.IR2026
Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers
Yue Kang, Zhuoyi Huang, Benji Schussheim +19
In enterprise search, building high-quality datasets at scale remains a central challenge due to the difficulty of acquiring labeled data. To resolve this challenge, we propose an…
cs.CL2025
GRAIL: Gradient-Based Adaptive Unlearning for Privacy and Copyright in LLMs
Kun-Woo Kim, Ji-Hoon Park, Ju-Min Han +1
Large Language Models (LLMs) trained on extensive datasets often learn sensitive information, which raises significant social and legal concerns under principles such as the "Right…