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
The representation landscape of few-shot learning and fine-tuning in large language models
Diego Doimo, Alessandro Serra, Alessio Ansuini, Alberto Cazzaniga
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities
Wei Lu, Rachel K. Luu, Markus J. Buehler
Activation function optimization method: Learnable series linear units (LSLUs)
Chuan Feng, Xi Lin, Shiping Zhu, Hongkang Shi, Maojie Tang, Hua Huang
3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability
Baohao Liao, Christof Monz
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization
Qianyi Zhao, Chen Qu, Cen Chen, Mingyuan Fan, Yanhao Wang
UNA: Unifying Alignments of RLHF/PPO, DPO and KTO by a Generalized Implicit Reward Function
Zhichao Wang, Bin Bi, Can Huang, Shiva Kumar Pentyala, Zixu James Zhu, Sitaram Asur, Na Claire Cheng
Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models
Wenxuan Zhang, Philip H. S. Torr, Mohamed Elhoseiny, Adel Bibi
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov, Egor Venediktov, Mariya Krylova, Aleksandr Zuev, Evgeny Burnaev
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task
Lingyun Huang, Jianxu Mao, Yaonan Wang, Junfei Yi, Ziming Tao
Non-instructional Fine-tuning: Enabling Instruction-Following Capabilities in Pre-trained Language Models without Instruction-Following Data
Juncheng Xie, Shensian Syu, Hung-yi Lee
PAT: Pruning-Aware Tuning for Large Language Models
Yijiang Liu, Huanrui Yang, Youxin Chen, Rongyu Zhang, Miao Wang, Yuan Du, Li Du
Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes
Sota Kato, Hinako Mitsuoka, Kazuhiro Hotta
Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models
Shenglin Zhang, Pengtian Zhu, Minghua Ma, Jiagang Wang, Yongqian Sun, Dongwen Li, Jingyu Wang, Qianying Guo, Xiaolei Hua, Lin Zhu, Dan Pei