Author

Jeongwan Shin

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Preprint Aug 2026

Can Language Models Understand mmWave Data? Benchmarking Large Language Models for mmWave Radar-Based Human Understanding

Large language models (LLMs) have shown remarkable reasoning and generative capabilities, motivating their use as universal reasoning engines for perception. While modern approaches such as vision-language models (VLMs) have attempted to incorporate reasoning capabilities into visual sensing, the integration of LLMs wi...

Jeongwan Shin, Jaehyeon Kim, Donguk Ko et al. · 1 citation · ⚡1

#artificial intelligence Review Sep 2026

KoNeoBench: A Curated Evaluation Dataset for LLM Understanding of Korean Neologisms

Large language models (LLMs) are typically evaluated on static benchmarks, even though natural language constantly evolves through newly emerging words and meanings. Existing Korean benchmarks are centered on established vocabulary and therefore provide limited coverage of such recent lexical change, and their English-...

Soha Lee, Soojin Lee, Heesung Yang et al. · 0 citations