May 9, 2024, 4:47 a.m. | Zheyan Qu, Lu Yin, Zitong Yu, Wenbo Wang, Xing zhang

cs.CL updates on arXiv.org arxiv.org

arXiv:2405.04781v1 Announce Type: new
Abstract: Large language models (LLMs) have demonstrated astonishing capabilities in natural language processing (NLP) tasks, sparking interest in their application to professional domains with higher specialized requirements. However, restricted access to closed-source LLMs via APIs and the difficulty in collecting massive high-quality datasets pose obstacles to the development of large language models in education fields of various courses. Given these challenges, we propose CourseGPT-zh, a course-oriented education LLM that supports customization and low-cost deployment. To address …

abstract access apis application arxiv capabilities cs.cl datasets distillation domains educational however knowledge language language model language models language processing large language large language model large language models llms massive natural natural language natural language processing nlp optimization processing professional prompt quality requirements restricted access tasks type via

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