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
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models
Shaojin Ding, David Qiu, David Rim, Yanzhang He, Oleg Rybakov, Bo Li, Rohit Prabhavalkar, Weiran Wang, Tara N. Sainath, Zhonglin Han, Jian Li, Amir Yazdanbakhsh, Shivani Agrawal
Distributed Inference and Fine-tuning of Large Language Models Over The Internet
Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers, Younes Belkada, Pavel Samygin, Colin Raffel
Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning
Lifeng Han, Serge Gladkoff, Gleb Erofeev, Irina Sorokina, Betty Galiano, Goran Nenadic
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models
Ibtihel Amara, Vinija Jain, Aman Chadha
Rethinking the Instruction Quality: LIFT is What You Need
Yang Xu, Yongqiang Yao, Yufan Huang, Mengnan Qi, Maoquan Wang, Bin Gu, Neel Sundaresan
Efficient End-to-end Language Model Fine-tuning on Graphs
Rui Xue, Xipeng Shen, Ruozhou Yu, Xiaorui Liu
Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning
Yongqi Dong, Xingmin Lu, Ruohan Li, Wei Song, Bart van Arem, Haneen Farah
Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
Zhixiang Wei, Lin Chen, Yi Jin, Xiaoxiao Ma, Tianle Liu, Pengyang Ling, Ben Wang, Huaian Chen, Jinjin Zheng
Hyperparameter Optimization for Large Language Model Instruction-Tuning
Christophe Tribes, Sacha Benarroch-Lelong, Peng Lu, Ivan Kobyzev
Generative Parameter-Efficient Fine-Tuning
Chinmay Savadikar, Xi Song, Tianfu Wu
Generalized Label-Efficient 3D Scene Parsing via Hierarchical Feature Aligned Pre-Training and Region-Aware Fine-tuning
Kangcheng Liu, Yong-Jin Liu, Kai Tang, Ming Liu, Baoquan Chen
PEFTDebias : Capturing debiasing information using PEFTs
Sumit Agarwal, Aditya Srikanth Veerubhotla, Srijan Bansal