5 citations · 15 across the 16 of their papers we have counts for
16 papers
Structured Gradient Guidance for Few-Shot Adaptation in Large Language Models
Hongye Zheng, Yichen Wang, Ray Pan +3
This paper presents a gradient-informed fine-tuning method for large language models under few-shot conditions. The goal is to enhance task adaptability and training stability when…
Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models
Jacky He, Guiran Liu, Binrong Zhu +3
This paper focuses on the dynamic optimization of the Retrieval-Augmented Generation (RAG) architecture. It proposes a state-aware dynamic knowledge retrieval mechanism to enhance…
Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction
Xiaokai Wang, Guiran Liu, Binrong Zhu +3
This study proposes a medical entity extraction method based on Transformer to enhance the information extraction capability of medical literature. Considering the professionalism…
Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM
Zhou Fang, Hanlu Zhang, Jacky He +2
This study aims to develop an efficient and accurate model for detecting malicious comments, addressing the increasingly severe issue of false and harmful content on social media p…
A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization
Dan Sun, Jacky He, Hanlu Zhang +3
This paper proposes a medical text summarization method based on LongFormer, aimed at addressing the challenges faced by existing models when processing long medical texts. Traditi…
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…
Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models
Zhen Qi, Jiajing Chen, Shuo Wang +3
This study aims to explore the performance improvement method of large language models based on GPT-4 under the multi-task learning framework and conducts experiments on two tasks:…
Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training
Jiajing Chen, Bingying Liu, Xiaoxuan Liao +3
With the rapid development of natural language processing technology, large-scale language models (LLM) have achieved remarkable results in a variety of tasks. However, how to effe…
A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation
Jiajing Chen, Shuo Wang, Zhen Qi +3
This research introduces a novel text generation model that combines BERT's semantic interpretation strengths with GPT-4's generative capabilities, establishing a high standard in…
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation
Yuxin Dong, Shuo Wang, Hongye Zheng +3
This study aims to optimize the existing retrieval-augmented generation model (RAG) by introducing a graph structure to improve the performance of the model in dealing with complex…
Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems
Jiajing Chen, Runyuan Bao, Hongye Zheng +3
This study aims to improve the accuracy and quality of large-scale language models (LLMs) in answering questions by integrating Elasticsearch into the Retrieval Augmented Generatio…