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
STAT: Shrinking Transformers After Training
Megan Flynn, Alexander Wang, Dean Edward Alvarez, Christopher De Sa, Anil Damle
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification
Michail Mamalakis, Héloïse de Vareilles, Shun-Chin Jim Wu, Ingrid Agartz, Lynn Egeland Mørch-Johnsen, Jane Garrison, Jon Simons, Pietro Lio, John Suckling, Graham Murray
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension
Runfeng Lin, Dacheng Xu, Huijiang Wang, Zebiao Chen, Yating Wang, Shouqiang Liu
Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin, Ling Liu
Low-rank finetuning for LLMs: A fairness perspective
Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza, Bhavya Kailkhura, Ferdinando Fioretto
OwLore: Outlier-weighed Layerwise Sampled Low-Rank Projection for Memory-Efficient LLM Fine-tuning
Pengxiang Li, Lu Yin, Xiaowei Gao, Shiwei Liu
Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosis
Mingyuan Liu, Lu Xu, Shengnan Liu, Jicong Zhang
DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution
Yulong Mao, Kaiyu Huang, Changhao Guan, Ganglin Bao, Fengran Mo, Jinan Xu
Spectral-Refiner: Fine-Tuning of Accurate Spatiotemporal Neural Operator for Turbulent Flows
Shuhao Cao, Francesco Brarda, Ruipeng Li, Yuanzhe Xi
On Fairness of Low-Rank Adaptation of Large Models
Zhoujie Ding, Ken Ziyu Liu, Pura Peetathawatchai, Berivan Isik, Sanmi Koyejo
Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
Akiyoshi Tomihari, Issei Sato
RLSF: Reinforcement Learning via Symbolic Feedback
Piyush Jha, Prithwish Jana, Pranavkrishna Suresh, Arnav Arora, Vijay Ganesh
A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang, Pin-Yu Chen, Christopher Carothers
No Two Devils Alike: Unveiling Distinct Mechanisms of Fine-tuning Attacks
Chak Tou Leong, Yi Cheng, Kaishuai Xu, Jian Wang, Hanlin Wang, Wenjie Li
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
Xudong Lu, Aojun Zhou, Yuhui Xu, Renrui Zhang, Peng Gao, Hongsheng Li
Feature Protection For Out-of-distribution Generalization
Lu Tan, Huei Zhou, Yinxiang Huang, Zeming Zheng, Yujiu Yang
MallowsPO: Fine-Tune Your LLM with Preference Dispersions
Haoxian Chen, Hanyang Zhao, Henry Lam, David Yao, Wenpin Tang
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtarik