10 citations · 16 across the 5 of their papers we have counts for
5 papers
MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models
Mianxin Liu, Jinru Ding, Jie Xu +16
Ensuring the general efficacy and goodness for human beings from medical large language models (LLM) before real-world deployment is crucial. However, a widely accepted and accessi…
LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation
Xiaoming Shi, Jie Xu, Jinru Ding +9
There is an increasing interest in developing LLMs for medical diagnosis to improve diagnosis efficiency. Despite their alluring technological potential, there is no unified and co…
MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine
Jie Xu, Lu Lu, Sen Yang +10
METHODS: First, a set of evaluation criteria is designed based on a comprehensive literature review. Second, existing candidate criteria are optimized for using a Delphi method by…
Meta-Path-Free Representation Learning on Heterogeneous Networks
Jie Zhang, Jinru Ding, Suyuan Liu +1
Real-world networks and knowledge graphs are usually heterogeneous networks. Representation learning on heterogeneous networks is not only a popular but a pragmatic research field.…
Entity Profiling in Knowledge Graphs
Xiang Zhang, Qingqing Yang, Jinru Ding +1
Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the ana…