Papers by Hongtu Zhu
LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts (2025.findings-emnlp)
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| Challenge: | Clinical trials are costly and pivotal processes that require substantial expenses . a new approach to integrate multimodal data for clinical outcome prediction is needed . |
| Approach: | a proposed framework transforms modality-specific data into natural language descriptions . a sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities . |
| Outcome: | a proposed framework outperforms baseline methods in predicting clinical trial outcomes . it transforms modality-specific data into natural language descriptions, encoded via unified encoders . |
Breach in the Shield: Unveiling the Vulnerabilities of Large Language Models (2026.eacl-long)
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| Challenge: | Large Language Models and Vision Language Model (LLMs) have impressive performance across a wide range of tasks, yet remain vulnerable to external and internal perturbations. |
| Approach: | They propose a stability measure called FI, First order local Influence, which quantifies the sensitivity of individual parameters and input dimensions. |
| Outcome: | The proposed stability measure measures the sensitivity of parameters and input dimensions across LLMs and VLMs. |
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models (2024.emnlp-main)
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| Challenge: | Existing medical large vision language models often generate inaccurate and irrelevant answers that do not align with established medical facts. |
| Approach: | They propose a strategy for controlling factuality risk through calibrated selection of the number of retrieved contexts and a preference dataset to fine-tune the model. |
| Outcome: | The proposed model achieves an average improvement of 20.8% on three medical VQA datasets. |
MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)
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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao
| Challenge: | Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence. |
| Approach: | They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory. |
| Outcome: | The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%. |
R1-RE: Cross-Domain Relation Extraction with RLVR (2026.acl-long)
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| Challenge: | Relation extraction (RE) is a core task in natural language processing. |
| Approach: | They propose a supervised learning task for relation extraction (RE) based on annotation guidelines. |
| Outcome: | The proposed model achieves an average OOD accuracy of 70%, on par with leading proprietary models such as GPT-4o. |