most citedEnhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

22 citations · 22 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

TCLeaf-Net: a transformer-convolution framework with global-local attention for robust in-field lesion-level plant leaf disease detection

Zishen Song, Yongjian Zhu, Dong Wang +6

Timely and accurate detection of foliar diseases is vital for safeguarding crop growth and reducing yield losses. Yet, in real-field conditions, cluttered backgrounds, domain shift…

cs.SI2025

GRAPHIA: Harnessing Social Graph Data to Enhance LLM-Based Social Simulation

Jiarui Ji, Zehua Zhang, Zhewei Wei +3

Large language models (LLMs) have shown promise in simulating human-like social behaviors. Social graphs provide high-quality supervision signals that encode both local interaction…

cs.CV2024

Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection

Hu Cao, Zehua Zhang, Yan Xia +4

In frame-based vision, object detection faces substantial performance degradation under challenging conditions due to the limited sensing capability of conventional cameras. Event…

cs.CL202422 cited

Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

Jiarui Li, Ye Yuan, Zehua Zhang

We proposed an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) for domain-specific a…

cs.LG2024

Pretraining Strategy for Neural Potentials

Zehua Zhang, Zijie Li, Amir Barati Farimani

We propose a mask pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems. GNNs are pre…