collaborators

13 papers

cs.CL2026

Characterizing Rhetorical Misalignment in Decision-Making with Language Models

Zirui Cheng, Joey Chan, Simo Du +3

Human decision-making is often shaped by a range of well-documented cognitive biases. As large language models (LLMs) become increasingly integrated into high-stakes human-AI decis…

cs.CL2026

Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering

Jash Rajesh Parekh, Wonbin Kweon, Joey Chan +7

Current biomedical question answering (QA) systems often assume that medical knowledge applies uniformly, yet real-world clinical reasoning is inherently conditional: nearly every…

cs.CL2026

Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution

Qiao Jin, Yin Fang, Lauren He +12

Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language models (LLMs) have the potential to automat…

cs.CL2026

MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering

Rezarta Islamaj, Robert Leaman, Joey Chan +13

Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain discriminative as model capabil…

cs.CL2026

Overview of the MedHopQA track at BioCreative IX: track description, participation and evaluation of systems for multi-hop medical question answering

Rezarta Islamaj, Joey Chan, Robert Leaman +13

Multi-hop question answering (QA) remains a significant challenge in the biomedical domain, requiring systems to integrate information across multiple sources to answer complex que…

cs.CL2026

ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?

Joey Chan, Yikun Han, Jingyuan Chen +8

Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores differences in readers' info…