6 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing
Minglai Yang, Xinyan Velocity Yu, Pengyuan Li +22
Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems. However, existing Optical Character Recognition (OC…
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…
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…
The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes
Lee Kezar, Elana Pontecorvo, Adele Daniels +6
Sign language recognition and translation technologies have the potential to increase access and inclusion of deaf signing communities, but research progress is bottlenecked by a l…