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cs.AI2026
Lifted State Hypothesis in Large Language Models
Bumjin Park, Jaesik Choi
Large language models (LLMs) adapt rapidly through fine-tuning and in-context learning, yet it remains unclear which inputs they treat as the same case and why their predictions ch…
cs.AI2026
Incomplete Prompt Jailbreaks in Large Language Models
Yeonjea Kim, Bumjin Park, Jaesik Choi
Large language models (LLMs) are increasingly released as open-weight models with safeguards against harmful requests. Nevertheless, sentence completion remains vulnerable to incom…
cs.CL2026
K-MetBench: A Multi-Dimensional Benchmark for Fine-Grained Evaluation of Expert Reasoning, Locality, and Multimodality in Meteorology
Soyeon Kim, Cheongwoong Kang, Myeongjin Lee +3
The development of practical (multimodal) large language model assistants for Korean weather forecasters is hindered by the absence of a multidimensional, expert-level evaluation f…