Papers by Sukwon Yun

4 papers
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)

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Challenge: Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains.
Approach: They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease.
Outcome: The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs.
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)

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Challenge: Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals.
Approach: They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training.
Outcome: The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training .
Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks (2025.acl-long)

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Challenge: Multi-agent LLMs are prone to adversarial attacks because of constraints such as limited token bandwidth and latency between message delivery.
Approach: They propose a permutation-invariant adversarial attack that optimizes prompt distribution across latency and bandwidth constraints to bypass distributed safety mechanisms within the system.
Outcome: The proposed method outperforms conventional attacks by up to 7 on multiple models.
Spatial Coordinates as a Cell Language: A Multi-Sentence Framework for Imaging Mass Cytometry Analysis (2025.findings-acl)

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Challenge: Existing single-cell LLMs struggle to integrate spatial information into natural language, limiting their ability to capture biological relationships.
Approach: They propose a framework that integrates both single-cell expression and spatial information into natural language using a multi-sentence approach.
Outcome: The proposed framework outperforms existing single-cell LLMs on preprocessed IMC datasets for diabetes and brain tumors while improving interpretability.

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