Task Specific Model
Task-specific models aim to optimize performance on individual tasks by tailoring model architectures and training data to specific needs, rather than relying on general-purpose models. Current research focuses on improving efficiency and generalization through techniques like model merging (combining multiple task-specific models), instruction tuning (adapting models via natural language instructions), and the use of Mixture-of-Experts (MoE) architectures for handling diverse data. This work is significant because it addresses the limitations of general-purpose models in specialized domains and offers more efficient and adaptable solutions for various applications, including natural language processing, computer vision, and robotics.
Papers
Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning
Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig
Agile Mobility with Rapid Online Adaptation via Meta-learning and Uncertainty-aware MPPI
Dvij Kalaria, Haoru Xue, Wenli Xiao, Tony Tao, Guanya Shi, John M. Dolan
From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning
Yang Bai, Yang Zhou, Jun Zhou, Rick Siow Mong Goh, Daniel Shu Wei Ting, Yong Liu
AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning
Jiaxi Cui, Wentao Zhang, Jing Tang, Xudong Tong, Zhenwei Zhang, Amie, Jing Wen, Rongsheng Wang, Pengfei Wu
Fine-Tuning Linear Layers Only Is a Simple yet Effective Way for Task Arithmetic
Ruochen Jin, Bojian Hou, Jiancong Xiao, Weijie Su, Li Shen
AutoTask: Task Aware Multi-Faceted Single Model for Multi-Task Ads Relevance
Shouchang Guo, Sonam Damani, Keng-hao Chang