June 7, 2024, 4:51 a.m. | Kaiyan Zhang, Sihang Zeng, Ermo Hua, Ning Ding, Zhang-Ren Chen, Zhiyuan Ma, Haoxin Li, Ganqu Cui, Biqing Qi, Xuekai Zhu, Xingtai Lv, Hu Jinfang, Zhiyu

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.03949v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas. Recent advanced proprietary models such as GPT-4 and Gemini have achieved significant advancements in biomedicine, which have also raised privacy and security challenges. The construction of specialized generalists hinges largely on high-quality datasets, enhanced by techniques like supervised fine-tuning and reinforcement learning from human or AI feedback, and direct preference optimization. However, these leading technologies (e.g., preference …

abstract advanced arxiv biomedicine building capabilities challenges construction cs.cl domains gemini gpt gpt-4 language language models large language large language models llms moving privacy privacy and security proprietary proprietary models security security challenges type

Senior Data Engineer

@ Displate | Warsaw

Junior Data Analyst - ESG Data

@ Institutional Shareholder Services | Mumbai

Intern Data Driven Development in Sensor Fusion for Autonomous Driving (f/m/x)

@ BMW Group | Munich, DE

Senior MLOps Engineer, Machine Learning Platform

@ GetYourGuide | Berlin

Data Engineer, Analytics

@ Meta | Menlo Park, CA

Data Engineer

@ Meta | Menlo Park, CA