Explore a cutting-edge conference talk on the intersection of Mixture of Experts (MOE) and Large Language Models (LLMs) for multi-task medical applications. Delve into parameter-efficient fine-tuning techniques presented by authors Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, and Yefeng Zheng. Gain insights into how these advanced AI methodologies are being applied to improve efficiency and performance in various medical tasks. Learn about the potential impact of this research on the future of healthcare technology and AI-assisted medical decision-making.
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications - Lecture 1
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 T1.2 [fp] When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical App
Taught by
Association for Computing Machinery (ACM)
Related Courses
-
Fine-tuning Language Models for Business Tasks
-
Improving Accuracy of LLM Applications
-
Fine-Tuning LLMs: Best Practices and When to Go Small - Lecture 124
-
Data-efficient Fine-tuning for LLM-based Recommendation - SIGIR 2024 M1.6
-
Pre-training Mixtral MoE Model with SageMaker HyperPod - Fine-Tuning and Continued Pre-Training
-
Characterizing Communication in Distributed Parameter-Efficient Fine-Tuning for LLMs
