8 papers
A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Tasks
Xiaoxuan Liao, Binrong Zhu, Jacky He +3
With the development of deep learning technology, large language models have achieved remarkable results in many natural language processing tasks. However, these models still have…
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning
Jia Gao, Shuangquan Lyu, Guiran Liu +3
With the continuous development of natural language processing (NLP) technology, text classification tasks have been widely used in multiple application fields. However, obtaining…
Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models
Xiaoxuan Liao, Chihang Wang, Shicheng Zhou +3
This paper presents a novel methodology of fine-tuning for large language models-dynamic LoRA. Building from the standard Low-Rank Adaptation framework, this methodology further ad…
Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer
Jia Gao, Guiran Liu, Binrong Zhu +3
This paper studies a text classification algorithm based on an improved Transformer to improve the performance and efficiency of the model in text classification tasks. Aiming at t…
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models
Shuo Wang, Chihang Wang, Jia Gao +3
This study proposes a knowledge distillation algorithm based on large language models and feature alignment, aiming to effectively transfer the knowledge of large pre-trained model…
Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks
Jiacheng Hu, Xiaoxuan Liao, Jia Gao +3
This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the…