Papers by Honghan Wu
BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain (2025.findings-acl)
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| Challenge: | Existing benchmarks for multi-hop reasoning in biomedical domain are lacking . bioHopR provides benchmarks to evaluate multi-step reasoning in structured biomedic knowledge graphs . |
| Approach: | They propose a benchmark to evaluate multi-hop, multi-answer reasoning in biomedical knowledge graphs. |
| Outcome: | BioHopR evaluates multi-hop reasoning in biomedical knowledge graphs based on the PrimeKG model . it outperforms proprietary models and open-source biomedal models in 1-hop and 2-hop tasks . |
HARE: an entity and relation centric evaluation framework for histopathology reports (2025.findings-emnlp)
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| Challenge: | evaluating the clinical quality of medical domain automated text generation remains a challenge. |
| Approach: | They propose a framework for histopathology automated report evaluation that prioritizes clinically relevant content by aligning critical histo pathology entities and relations between reference and generated reports. |
| Outcome: | The proposed framework outperforms existing metrics in histopathology report evaluations. |
Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings (2025.acl-long)
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Imane Guellil, Salomé Andres, Atul Anand, Bruce Guthrie, Huayu Zhang, Abul Hasan, Honghan Wu, Beatrice Alex
| Challenge: | Existing resources for AE extraction are limited due to complexity, variability, and ambiguity of clinical narratives. |
| Approach: | They present a manually annotated corpus for Adverse Event (AE) extraction from discharge summaries of elderly patients. |
| Outcome: | The proposed model performs well on coarse-grained extraction, but drops notably for rare events and complex attributes. |
CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset (2024.findings-acl)
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| Challenge: | Legal Judgment Prediction (LJP) has attracted significant attention in recent years. |
| Approach: | They propose a large-scale Chinese Multi-Defendant LJP dataset . they propose case-level evaluation metrics dedicated for the multi-defendant scenario . |
| Outcome: | The proposed methods show weaknesses when applied to cases involving multiple defendants. |
Look & Mark: Leveraging Radiologist Eye Fixations and Bounding boxes in Multimodal Large Language Models for Chest X-ray Report Generation (2025.findings-acl)
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| Challenge: | Recent advances in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, but still suffer from hallucinations and clinically significant errors. |
| Approach: | They propose a grounding fixation strategy that integrates radiologist eye fixations and bounding box annotations into the LLM prompting framework. |
| Outcome: | The proposed model improves performance without retraining across domain-specific and general-purpose models and achieves an 87.3% clinical average performance. |