1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2026
Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation
Liwen Sun, Xiang Yu, Ming Tan +4
Clinical diagnosis is time-consuming, requiring intensive interactions between patients and medical professionals. While large language models (LLMs) could ease the pre-diagnostic…
cs.CL2024
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models
Zhen Zhang, Xinyu Wang, Yong Jiang +7
Large Language Models (LLMs) often struggle with dynamically changing knowledge and handling unknown static information. Retrieval-Augmented Generation (RAG) is employed to tackle…
cs.CL2024★ 1 cited
Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
Xiaoze Liu, Feijie Wu, Tianyang Xu +4
The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern fo…