3 papers
cs.LG2026
SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei +3
Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous knowledge, without costly full…
cs.CL2025
Phonological Representation Learning for Isolated Signs Improves Out-of-Vocabulary Generalization
Lee Kezar, Zed Sehyr, Jesse Thomason
Sign language datasets are often not representative in terms of vocabulary, underscoring the need for models that generalize to unseen signs. Vector quantization is a promising app…
cs.CL2024
The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge
Lee Kezar, Nidhi Munikote, Zian Zeng +3
Language models for American Sign Language (ASL) could make language technologies substantially more accessible to those who sign. To train models on tasks such as isolated sign re…