Model Performance
Model performance research focuses on improving the accuracy, efficiency, and robustness of machine learning models across diverse applications. Current efforts concentrate on optimizing ensemble methods, particularly for large language models (LLMs), and addressing challenges like model drift and the impact of data quality and quantity on performance, often employing techniques like network deconvolution, adaptive sampling, and low-rank adaptation. These advancements are crucial for deploying reliable AI systems in various fields, from healthcare diagnostics to resource-constrained IoT devices, and for establishing robust evaluation methodologies to ensure trustworthy AI.
Papers
Hybrid Training Approaches for LLMs: Leveraging Real and Synthetic Data to Enhance Model Performance in Domain-Specific Applications
Alexey Zhezherau, Alexei Yanockin
MYCROFT: Towards Effective and Efficient External Data Augmentation
Zain Sarwar, Van Tran, Arjun Nitin Bhagoji, Nick Feamster, Ben Y. Zhao, Supriyo Chakraborty
Empirical Insights on Fine-Tuning Large Language Models for Question-Answering
Junjie Ye, Yuming Yang, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan
A Comprehensive Evaluation of Large Language Models on Mental Illnesses
Abdelrahman Hanafi, Mohammed Saad, Noureldin Zahran, Radwa J. Hanafy, Mohammed E. Fouda