102 citations · 547 across the 60 of their papers we have counts for
9 papers · 1 filter
Customized FinGPT Search Agents Using Foundation Models
Felix Tian, Ajay Byadgi, Daniel Kim +4
Current large language models (LLMs) have proven useful for analyzing financial data, but most existing models, such as BloombergGPT and FinGPT, lack customization for specific use…
Large Language Models for Disease Diagnosis: A Scoping Review
Shuang Zhou, Zidu Xu, Mian Zhang +14
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intellige…
GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models
Yi Fang, Dongzhe Fan, Daochen Zha +1
This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes. Unlike traditional graph contrastive methods t…
GraphFM: A Comprehensive Benchmark for Graph Foundation Model
Yuhao Xu, Xinqi Liu, Keyu Duan +4
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream…
Denoising-Aware Contrastive Learning for Noisy Time Series
Shuang Zhou, Daochen Zha, Xiao Shen +3
Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is l…
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
Yu-Neng Chuang, Songchen Li, Jiayi Yuan +11
Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time…
Cost-efficient Knowledge-based Question Answering with Large Language Models
Junnan Dong, Qinggang Zhang, Chuang Zhou +3
Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their…
E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification
Xin Zhang, Daochen Zha, Qiaoyu Tan
This work studies ensemble learning for graph neural networks (GNNs) under the popular semi-supervised setting. Ensemble learning has shown superiority in improving the accuracy an…
Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering
Junnan Dong, Qinggang Zhang, Huachi Zhou +3
Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attemp…