11 citations · 35 across the 18 of their papers we have counts for
19 papers
StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents
Haojie Hao, Longkun Hao, Yihang Lou +8
Reinforcement Learning (RL) has become a promising approach for improving GUI Agents in long-horizon, stochastic digital environments, but trajectory-level success feedback is too…
When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs
Zhichao Yang, Caiqi Zhang, Ruihan Yang +3
Calibration evaluates whether a model confidence aligns with its empirical accuracy. Existing studies often compare the calibration of different large language models using global…
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…
TARSE: Test-Time Adaptation via Retrieval of Skills and Experience for Reasoning Agents
Junda Wang, Zonghai Tao, Hansi Zeng +3
Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior exa…
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…
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…
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…
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…
MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills
Zonghai Yao, Zihao Zhang, Chaolong Tang +8
Artificial intelligence (AI) and large language models (LLMs) in healthcare require advanced clinical skills (CS), yet current benchmarks fail to evaluate these comprehensively. We…
UMass-BioNLP at MEDIQA-M3G 2024: DermPrompt -- A Systematic Exploration of Prompt Engineering with GPT-4V for Dermatological Diagnosis
Parth Vashisht, Abhilasha Lodha, Mukta Maddipatla +6
This paper presents our team's participation in the MEDIQA-ClinicalNLP2024 shared task B. We present a novel approach to diagnosing clinical dermatology cases by integrating large…
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
Zhichao Yang, Avijit Mitra, Sunjae Kwon +1
The advancement of natural language processing (NLP) systems in healthcare hinges on language model ability to interpret the intricate information contained within clinical notes.…
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability
Junda Wang, Zhichao Yang, Zonghai Yao +1
Large Language Models (LLMs) have demonstrated a remarkable potential in medical knowledge acquisition and question-answering. However, LLMs can potentially hallucinate and yield f…
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP
Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei +6
The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Recor…
Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation
Zonghai Yao, Ahmed Jaafar, Beining Wang +2
This study examines the effect of prompt engineering on the performance of Large Language Models (LLMs) in clinical note generation. We introduce an Automatic Prompt Optimization (…
Large Language Models are In-context Teachers for Knowledge Reasoning
Jiachen Zhao, Zonghai Yao, Zhichao Yang +1
In this work, we study in-context teaching (ICT), where a teacher provides in-context example rationales to teach a student to reason over unseen cases. Human teachers are usually…
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing
Hieu Tran, Zhichao Yang, Zonghai Yao +1
To enhance the performance of large language models (LLMs) in biomedical natural language processing (BioNLP) by introducing a domain-specific instruction dataset and examining its…
NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
Junda Wang, Zonghai Yao, Zhichao Yang +5
We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues. NoteChat embodies the principle th…