20 citations · 93 across the 46 of their papers we have counts for
51 papers
Measure First, Then Allocate: Calibrated Rollout Budgets for MCP Agents in Reinforcement Learning
Hieu Tran, Zonghai Yao, Sunjae Kwon +1
Training tool-using language models requires costly interaction, yet identical rewards within a rollout group provide no group-relative learning signal. We use reward disagreement…
OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction
Mingchen Li, Rohan Pandey, Junhui Qian +5
Opioid overdose remains a major clinical and public health burden, highlighting the need for scalable approaches to identify patients at high risk. Here, we investigate diagnosis-s…
DischargeBench: Pre-Deployment Evaluation of LLM Discharge Educators through Persona-Grounded Patient Simulation
Won Seok Jang, Zonghai Yao, Youngbin Jang +3
Effective discharge education is personalized: a clinician adapts the discharge plan to each patient's literacy, recall, and personality. Before large language models (LLMs) take o…
Medical thinking with multiple images
Zonghai Yao, Benlu Wang, Yifan Zhang +8
Large language models perform well on many medical QA benchmarks, but real clinical reasoning often requires integrating evidence across multiple images rather than interpreting a…
Rethinking Patient Education as Multi-turn Multi-modal Interaction
Zonghai Yao, Zhipeng Tang, Chengtao Lin +5
Most medical multimodal benchmarks focus on static tasks such as image question answering, report generation, and plain-language rewriting. Patient education is more demanding: sys…
Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction
Mingchen Li, Jiatan Huang, Zonghai Yao +1
Large language models (LLMs) hold significant promise for healthcare, yet their reliability in high-stakes clinical settings is often compromised by hallucinations and a lack of gr…
Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty
Sravanthi Machcha, Sushrita Yerra, Sahil Gupta +4
Current evaluation of large language models (LLMs) overwhelmingly prioritizes accuracy; however, in real-world and safety-critical applications, the ability to abstain when uncerta…
Iterative Critique-Refine Framework for Enhancing LLM Personalization
Durga Prasad Maram, Dhruvin Gandhi, Zonghai Yao +5
Personalized text generation requires models not only to produce coherent text but also to align with a target user's style, tone, and topical focus. Existing retrieval-augmented a…
Chatbot To Help Patients Understand Their Health
Won Seok Jang, Hieu Tran, Manav Mistry +7
Patients must possess the knowledge necessary to actively participate in their care. We present NoteAid-Chatbot, a conversational AI that promotes patient understanding via a novel…
Exploiting Tree Structure for Credit Assignment in RL Training of LLMs
Hieu Tran, Zonghai Yao, Hong Yu
Reinforcement learning improves LLM reasoning, yet sparse delayed reward over long sequences makes token-level credit assignment the key bottleneck. We study the verifiable-reward…
DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Discharge
Zonghai Yao, Michael Sun, Won Seok Jang +3
Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. While recent large language model (LLM) benchm…
MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework
Md Shahidul Salim, Lian Fu, Arav Adikesh Ramakrishnan +2
We present MedCOD (Medical Chain-of-Dictionary), a hybrid framework designed to improve English-to-Spanish medical translation by integrating domain-specific structured knowledge i…
PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning
Hieu Tran, Zonghai Yao, Nguyen Luong Tran +5
Inspired by the dual-process theory of human cognition from \textit{Thinking, Fast and Slow}, we introduce \textbf{PRIME} (Planning and Retrieval-Integrated Memory for Enhanced Rea…
From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations
Benlu Wang, Iris Xia, Yifan Zhang +6
Large language models (LLMs) have demonstrated promising performance on medical benchmarks; however, their ability to perform medical calculations, a crucial aspect of clinical dec…
ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care
Zonghai Yao, Talha Chafekar, Junda Wang +5
Real-world adoption of closed-loop insulin delivery systems (CLIDS) in type 1 diabetes remains low, driven not by technical failure, but by diverse behavioral, psychosocial, and so…
ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recovery
Junda Wang, Zonghai Yao, Lingxi Li +3
Substance use disorders (SUDs) affect millions of people, and relapses are common, requiring multi-session treatments. Access to care is limited, which contributes to the challenge…
SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Data
Zonghai Yao, Youxia Zhao, Avijit Mitra +4
Eviction is a significant yet understudied social determinants of health (SDoH), linked to housing instability, unemployment, and mental health. While eviction appears in unstructu…
MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning
Hieu Tran, Zonghai Yao, Won Seok Jang +4
Generative AI has demonstrated strong potential in healthcare, from clinical decision support to patient-facing chatbots that improve outcomes. A critical challenge for deployment…
Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study
Won Seok Jang, Sharmin Sultana, Zonghai Yao +4
OpenNotes gives patients access to their EHR notes, but dense medical jargon limits comprehension. We evaluate closed-source and open-source LLMs for extracting and prioritizing th…
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models
Hieu Tran, Zonghai Yao, Junda Wang +3
This work introduces RARE (Retrieval-Augmented Reasoning Enhancement), a versatile extension to the mutual reasoning framework (rStar), aimed at enhancing reasoning accuracy and fa…
Blocks Architecture (BloArk): Efficient, Cost-Effective, and Incremental Dataset Architecture for Wikipedia Revision History
Lingxi Li, Zonghai Yao, Sunjae Kwon +1
Wikipedia (Wiki) is one of the most widely used and publicly available resources for natural language processing (NLP) applications. Wikipedia Revision History (WikiRevHist) shows…
CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing Constraints
Anirudh Atmakuru, Jatin Nainani, Rohith Siddhartha Reddy Bheemreddy +4
Evaluating the creativity of large language models (LLMs) in story writing is difficult because LLM-generated stories could seemingly look creative but be very similar to some exis…
RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine
Jiatan Huang, Mingchen Li, Zonghai Yao +8
Answering complex real-world questions in the medical domain often requires accurate retrieval from medical Textual Knowledge Graphs (medical TKGs), as the relational path informat…
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback
Zonghai Yao, Aditya Parashar, Huixue Zhou +4
Automatic question generation (QG) is essential for AI and NLP, particularly in intelligent tutoring, dialogue systems, and fact verification. Generating multiple-choice questions…