Papers by Sunguk Shin
SGT: Securing Open-Source LLMs Against Malicious Fine-tuning via Safety Guidance Trigger (2026.acl-long)
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| Challenge: | open-weight large language models increase vulnerability to malicious fine-tuning . despite these advantages, open-source LLMs increase the potential for misuse . |
| Approach: | They propose a safety guide for open-weight large language models that guides fine-tuning toward the safety manifold to preserve alignment. |
| Outcome: | The proposed safety guidance trigger significantly improves robustness against malicious fine-tuning. |
GraphMind: LLMs as Dynamic Knowledge Builders for Sequential Decision-Making (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance in natural language understanding and generation, establishing themselves as foundational tools across a wide range of domains. |
| Approach: | They propose an LLM agent architecture that integrates a knowledge graph as a graph-based memory module and integrates it into the agent to generate efficient plans. |
| Outcome: | The proposed architecture improves the performance and efficiency of the LLM in navigation tasks designed to present long-horizon and partially observable challenges. |