11 citations · 20 across the 5 of their papers we have counts for
5 papers
LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning
Yining Huang, Bin Li, Keke Tang +1
Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought (CoT) reasoning, yet pushing their performance typically requires vast data…
A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry
Yining Huang, Keke Tang, Meilian Chen +1
Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs) such as GPT and BERT have evolved significantly, impacting various industries with their a…
Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies
Yining Huang, Keke Tang, Meilian Chen
Emerging Large Language Models (LLMs) like GPT-4 have revolutionized Natural Language Processing (NLP), showing potential in traditional tasks such as Named Entity Recognition (NER…
Sinoledge: A Knowledge Engine based on Logical Reasoning and Distributed Micro Services
Yining Huang, Shaoze Lin, Yijun Wei +1
We propose a knowledge engine called Sinoledge mainly for doctors, physicians, and researchers in medical field to organize thoughts, manage reasoning process, test and deploy to p…
Training like Playing: A Reinforcement Learning And Knowledge Graph-based framework for building Automatic Consultation System in Medical Field
Yining Huang, Meilian Chen, Keke Tang
We introduce a framework for AI-based medical consultation system with knowledge graph embedding and reinforcement learning components and its implement. Our implement of this fram…