Mingyu Ma — accepted AI-conference papers

2026

TwinWeaver: An LLM-Based Foundation Model Framework for Pan-Cancer Digital Twins

Nikita Makarov, Maria Bordukova, Lena von Voithenberg, Estrella Pivel-Villanueva, Sabrina Mielke, Jonathan Wickes +8

ICML 2026poster

Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce TwinWeaver, an open-source framework that serializes longitudinal patient histories into text, enabling unified event prediction …

Cited by 0 Source ↗Scholar ↗

2024

Improving Event Definition Following For Zero-Shot Event Detection

Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Ma, Hritik Bansal, Baobao Chang +3

ACL 2024long

Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches yield sporadic successes, yet generally fall short of expectations.In this work, we aim to improve zero-shot event detec…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2024

Instructional Fingerprinting of Large Language Models

Jiashu Xu, Fei Wang, Mingyu Ma, Pang Wei Koh, Chaowei Xiao, Muhao Chen

NAACL 2024long

The exorbitant cost of training Large language models (LLMs) from scratch makes it essential to fingerprint the models to protect intellectual property via ownership authentication and to ensure downstream users and developers comply with their license terms (eg restricting commercial use). In this…

Cited by 45 Source ↗PDF ↗code ↗Scholar ↗

2024

Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models

Jiashu Xu, Mingyu Ma, Fei Wang, Chaowei Xiao, Muhao Chen

NAACL 2024long

We investigate security concerns of the emergent instruction tuning paradigm, that models are trained on crowdsourced datasets with task instructions to achieve superior performance. Our studies demonstrate that an attacker can inject backdoors by issuing very few malicious instructions (~1000 token…

Cited by 129 Source ↗PDF ↗code ↗Scholar ↗

2024

Mitigating Bias for Question Answering Models by Tracking Bias Influence

Mingyu Ma, Jiun-Yu Kao, Arpit Gupta, Yu-Hsiang Lin, Wenbo Zhao, Tagyoung Chung +3

NAACL 2024long

Models of various NLP tasks have been shown to exhibit stereotypes, and the bias in the question answering (QA) models is especially harmful as the output answers might be directly consumed by the end users. There have been datasets to evaluate bias in QA models, while bias mitigation technique for…

Cited by 7 Source ↗PDF ↗Scholar ↗