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
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
Junyu Luo, Xiao Luo, Kaize Ding, Jingyang Yuan, Zhiping Xiao, Ming Zhang
ResoFilter: Rine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis
Zeao Tu, Xiangdi Meng, Yu He, Zihan Yao, Tianyu Qi, Jun Liu, Ming Li
Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning
Ziang Ye, Zhenru Zhang, Yang Zhang, Jianxin Ma, Junyang Lin, Fuli Feng
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning
Pramit Saha, Divyanshu Mishra, Felix Wagner, Konstantinos Kamnitsas, J. Alison Noble
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models
Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer
Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset
Bijay Adhikari, Pratibha Kulung, Jakesh Bohaju, Laxmi Kanta Poudel, Confidence Raymond, Dong Zhang, Udunna C Anazodo, Bishesh Khanal, Mahesh Shakya
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control
Wenchang Duan, Zhenguo Gao, Jiwan He, Jinguo Xian
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning
Jaeyoon Kim, Yoonki Cho, Taeyong Kim, Sung-Eui Yoon
LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning
Yansheng Mao, Jiaqi Li, Fanxu Meng, Jing Xiong, Zilong Zheng, Muhan Zhang
Rethink the Evaluation Protocol of Model Merging on Classification Task
Fanshuang Kong, Richong Zhang, Zhijie Nie, Ziqiao Wang
Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models
Xinxin Liu, Aaron Thomas, Cheng Zhang, Jianyi Cheng, Yiren Zhao, Xitong Gao
Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Model's Prediction Rationality
Qitong Wang, Tang Li, Kien X. Nguyen, Xi Peng
XTransplant: A Probe into the Upper Bound Performance of Multilingual Capability and Culture Adaptability in LLMs via Mutual Cross-lingual Feed-forward Transplantation
Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Libo Qin, Yichong Huang, Lei Huang, Weitao Ma, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin
MultiLingPoT: Enhancing Mathematical Reasoning with Multilingual Program Fine-tuning
Nianqi Li, Zujie Liang, Siyu Yuan, Jiaqing Liang, Feng Wei, Yanghua Xiao
NLSR: Neuron-Level Safety Realignment of Large Language Models Against Harmful Fine-Tuning
Xin Yi, Shunfan Zheng, Linlin Wang, Gerard de Melo, Xiaoling Wang, Liang He
Bridging the Gap: Enhancing LLM Performance for Low-Resource African Languages with New Benchmarks, Fine-Tuning, and Cultural Adjustments
Tuka Alhanai, Adam Kasumovic, Mohammad Ghassemi, Aven Zitzelberger, Jessica Lundin, Guillaume Chabot-Couture
Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing
Anika Tabassum, Amirkoushyar Ziabari