all AI news
Differentially Private Knowledge Distillation via Synthetic Text Generation
March 5, 2024, 2:41 p.m. | James Flemings, Murali Annavaram
cs.LG updates on arXiv.org arxiv.org
Abstract: Large Language models (LLMs) are achieving state-of-the-art performance in many different downstream tasks. However, the increasing urgency of data privacy requires LLMs to train with Differential Privacy (DP) on private data. Concurrently it is also necessary to compress LLMs for real-life deployments on resource-constrained devices or latency-sensitive applications. Differential privacy and model compression generally must trade off utility loss to achieve their objectives. Moreover, concurrently achieving both can result in even more utility loss. To …
abstract art arxiv cs.cl cs.cr cs.lg data data privacy deployments devices differential differential privacy distillation knowledge language language models large language large language models latency life llms performance privacy private data state synthetic tasks text text generation train type via
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US