6 papers
TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews
Huimin Xu, Seungjun Yi, Terence Lim +9
Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data. TA provides valuable insights in healthcare but is resource-i…
Benchmarking Multi-turn Medical Diagnosis: Hold, Lure, and Self-Correction
Jinrui Fang, Runhan Chen, Xu Yang +9
Large language models (LLMs) achieve high accuracy in medical diagnosis when all clinical information is provided in a single turn, yet how they behave under multi-turn evidence ac…
Rethinking the Value of Multi-Agent Workflow: A Strong Single Agent Baseline
Jiawei Xu, Arief Koesdwiady, Sisong Bei +8
Recent advances in LLM-based multi-agent systems (MAS) show that workflows composed of multiple LLM agents with distinct roles, tools, and communication patterns can outperform sin…
Interactive Graph Visualization and TeamingRecommendation in an Interdisciplinary Project'sTalent Knowledge Graph
Jiawei Xu, Juichien Chen, Yilin Ye +5
Interactive visualization of large scholarly knowledge graphs combined with LLM reasoning shows promise butremains under-explored. We address this gap by developing an interactive…
LLM-TA: An LLM-Enhanced Thematic Analysis Pipeline for Transcripts from Parents of Children with Congenital Heart Disease
Muhammad Zain Raza, Jiawei Xu, Terence Lim +4
Thematic Analysis (TA) is a fundamental method in healthcare research for analyzing transcript data, but it is resource-intensive and difficult to scale for large, complex datasets…
Demo: Interactive Visualization of Semantic Relationships in a Biomedical Project's Talent Knowledge Graph
Jiawei Xu, Zhandos Sembay, Swathi Thaker +3
We present an interactive visualization of the Cell Map for AI Talent Knowledge Graph (CM4AI TKG), a detailed semantic space comprising approximately 28,000 experts and 1,000 datas…