Open Source Large Language Model
Open-source large language models (LLMs) aim to provide accessible and customizable alternatives to proprietary models, fostering research and development while addressing concerns about data privacy and vendor lock-in. Current research focuses on adapting these models to specific languages and domains (e.g., Romanian, medicine, finance), improving their reasoning capabilities through techniques like retrieval-augmented generation and mixture-of-experts architectures, and optimizing their deployment efficiency on various hardware. This burgeoning field significantly impacts both the scientific community, by enabling broader participation in LLM research, and practical applications, offering cost-effective and adaptable solutions for diverse tasks ranging from question answering to code generation.
Papers
Efficient Prompting for LLM-based Generative Internet of Things
Bin Xiao, Burak Kantarci, Jiawen Kang, Dusit Niyato, Mohsen Guizani
A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations
Jinqiang Wang, Huansheng Ning, Yi Peng, Qikai Wei, Daniel Tesfai, Wenwei Mao, Tao Zhu, Runhe Huang
Representation Noising: A Defence Mechanism Against Harmful Finetuning
Domenic Rosati, Jan Wehner, Kai Williams, Łukasz Bartoszcze, David Atanasov, Robie Gonzales, Subhabrata Majumdar, Carsten Maple, Hassan Sajjad, Frank Rudzicz
MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability
Yanrui Du, Sendong Zhao, Danyang Zhao, Ming Ma, Yuhan Chen, Liangyu Huo, Qing Yang, Dongliang Xu, Bing Qin