Fine Tuning
Fine-tuning adapts pre-trained large language models (LLMs) to specific tasks, improving performance and efficiency compared to training from scratch. Current research emphasizes efficient fine-tuning methods like low-rank adaptation (LoRA) and techniques addressing challenges such as catastrophic forgetting and calibration issues, often employing bilevel optimization or adaptive noise allocation for improved performance and privacy. This work is significant because it enables the deployment of powerful LLMs across diverse applications, from medical diagnosis to visual editing, while mitigating resource constraints and privacy concerns.
Papers
Adapting Image-based RL Policies via Predicted Rewards
Weiyao Wang, Xinyuan Fang, Gregory D. Hager
From Imitation to Refinement -- Residual RL for Precise Visual Assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld, Marcel Torne, Pulkit Agrawal
Towards scalable efficient on-device ASR with transfer learning
Laxmi Pandey, Ke Li, Jinxi Guo, Debjyoti Paul, Arthur Guo, Jay Mahadeokar, Xuedong Zhang
Assessing In-context Learning and Fine-tuning for Topic Classification of German Web Data
Julian Schelb, Roberto Ulloa, Andreas Spitz
Crafting Efficient Fine-Tuning Strategies for Large Language Models
Michael Oliver, Guan Wang
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review
Masatoshi Uehara, Yulai Zhao, Tommaso Biancalani, Sergey Levine
Can Open-Source LLMs Compete with Commercial Models? Exploring the Few-Shot Performance of Current GPT Models in Biomedical Tasks
Samy Ateia, Udo Kruschwitz
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification
Naif Alkhunaizi, Faris Almalik, Rouqaiah Al-Refai, Muzammal Naseer, Karthik Nandakumar
Exploring connections of spectral analysis and transfer learning in medical imaging
Yucheng Lu, Dovile Juodelyte, Jonathan D. Victor, Veronika Cheplygina