Papers by Yunsoo Kim
Foundation Model for Biomedical Graphs: Integrating Knowledge Graphs and Protein Structures to Large Language Models (2024.acl-srw)
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| Challenge: | Transformer model has been a de-facto standard in natural language processing, but it is limited to images, text, and/or sequence data. |
| Approach: | They propose to use a multimodal large language model architecture to handle biomedical graphs such as protein structure and chemical molecules to improve its performance. |
| Outcome: | The proposed architecture can handle multiple data types for biomedical graphs such as protein structure and chemical molecules. |
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 . |
Chemical Language Understanding Benchmark (2023.acl-industry)
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| Challenge: | CLUB datasets are used to facilitate NLP research in the chemical industry. |
| Approach: | They introduce a benchmark dataset called CLUB to facilitate NLP research in the chemical industry. |
| Outcome: | The CLUB datasets are a new benchmark dataset for NLP in the chemical industry. |
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. |
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. |