35 citations · 116 across the 24 of their papers we have counts for
6 papers · 1 filter
Sci-CoT: Leveraging Large Language Models for Enhanced Knowledge Distillation in Small Models for Scientific QA
Yuhan Ma, Haiqi Jiang, Chenyou Fan
Large Language Models (LLMs) have shown outstanding performance across wide range of downstream tasks. This competency is attributed to their substantial parameter size and pre-tra…
Low-Parameter Federated Learning with Large Language Models
Jingang Jiang, Xiangyang Liu, Chenyou Fan
We study few-shot Natural Language Understanding (NLU) tasks with Large Language Models (LLMs) in federated learning (FL) scenarios. It is a challenging task due to limited labeled…
Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospects
Kehui Tan, Tianqi Pang, Chenyou Fan +1
This perspective paper proposes a series of interactive scenarios that utilize Artificial Intelligence (AI) to enhance classroom teaching, such as dialogue auto-completion, knowled…
Carbon Price Forecasting with Quantile Regression and Feature Selection
Tianqi Pang, Kehui Tan, Chenyou Fan
Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become crit…
Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering
Xiangyang Liu, Tianqi Pang, Chenyou Fan
We investigate how to enhance answer precision in frequently asked questions posed by distributed users using cloud-based Large Language Models (LLMs). Our study focuses on a typic…
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation
Junjie Hu, Chenyou Fan, Liguang Zhou +3
With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelo…