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W. Jang

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#artificial intelligence Preprint Oct 2026

Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?

It is reported that collaboration buys task performance but not lower hallucination as a rule, and no topology is reliably better than another; it is found that collaboration redistributes what its models can already do; it does not supply what they cannot.

W. Jang, Harry Bhaskar Joshi, Hong Yu · 0 citations
#artificial intelligence Preprint Mar 2026

BUZZY: Contrastive Scoring to Mitigate Text-Induced Bias in Multimodal Multiple-Choice QA

This work hypothesizes and empirically verify that a model genuinely relies on visual evidence only when its multimodal distribution significantly diverges from its text-only distribution and proposes BUZZY, a training-free decoding method that corrects multimodal predictions by subtracting the text-only distribution.

Taeyun Roh, Suhyeong Park, Dongyoung Lee et al. · 0 citations
Open access Feb 2025

Enhancing Large Language Models for Identifying and Prioritizing Important Medical Jargons From Electronic Health Record Notes Using Data Augmentation: Comparative Study

This study evaluated both closed-source and open-source large language models for extracting and prioritizing medical jargon from EHR notes relevant to individual patients, leveraging prompting techniques, fine-tuning, and data augmentation and found that model performance could deviate largely based on prompting style...

W. Jang, Sharmin Sultana, Zonghai Yao et al. · 1 citation

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