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
GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
Haoze Wu, Zihan Qiu, Zili Wang, Hang Zhao, Jie Fu
Interpretable Catastrophic Forgetting of Large Language Model Fine-tuning via Instruction Vector
Gangwei Jiang, Caigao Jiang, Zhaoyi Li, Siqiao Xue, Jun Zhou, Linqi Song, Defu Lian, Ying Wei
A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding
Gaëlle Laperrière, Sahar Ghannay, Bassam Jabaian, Yannick Estève
Tracking the perspectives of interacting language models
Hayden Helm, Brandon Duderstadt, Youngser Park, Carey E. Priebe
A Semantic-based Layer Freezing Approach to Efficient Fine-Tuning of Language Models
Jian Gu, Aldeida Aleti, Chunyang Chen, Hongyu Zhang
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs
Haifeng Wen, Hong Xing, Osvaldo Simeone
Mitigating Large Language Model Hallucination with Faithful Finetuning
Minda Hu, Bowei He, Yufei Wang, Liangyou Li, Chen Ma, Irwin King
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
Scott Barnett, Zac Brannelly, Stefanus Kurniawan, Sheng Wong
Concept-skill Transferability-based Data Selection for Large Vision-Language Models
Jaewoo Lee, Boyang Li, Sung Ju Hwang
Self-Evolution Fine-Tuning for Policy Optimization
Ruijun Chen, Jiehao Liang, Shiping Gao, Fanqi Wan, Xiaojun Quan
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning
Haoyu Wang, Tianci Liu, Ruirui Li, Monica Cheng, Tuo Zhao, Jing Gao
Comparison of fine-tuning strategies for transfer learning in medical image classification
Ana Davila, Jacinto Colan, Yasuhisa Hasegawa
Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
Jie Chen, Xintian Han, Yu Ma, Xun Zhou, Liang Xiang
Parameter-Efficient Active Learning for Foundational models
Athmanarayanan Lakshmi Narayanan, Ranganath Krishnan, Amrutha Machireddy, Mahesh Subedar
MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning
Hanqing Wang, Yixia Li, Shuo Wang, Guanhua Chen, Yun Chen
Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition
William Ravenscroft, George Close, Stefan Goetze, Thomas Hain, Mohammad Soleymanpour, Anurag Chowdhury, Mark C. Fuhs
Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models
Minghao Wu, Thuy-Trang Vu, Lizhen Qu, Gholamreza Haffari
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models
Francisco Eiras, Aleksandar Petrov, Phillip H. S. Torr, M. Pawan Kumar, Adel Bibi
Automated Information Extraction from Thyroid Operation Narrative: A Comparative Study of GPT-4 and Fine-tuned KoELECTRA
Dongsuk Jang, Hyeryun Park, Jiye Son, Hyeonuk Hwang, Sujin Kim, Jinwook Choi