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cs.CL2025
ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models
Junho Yoon, Geom Lee, Donghyeon Jeon +2
Quantization has been widely studied as an effective technique for reducing the memory requirement of large language models (LLMs), potentially improving the latency time as well.…
cs.CL2025
QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines
Ohjoon Kwon, Changsu Lee, Jihye Back +3
Large language models (LLMs) have been widely used for relevance assessment in information retrieval. However, our study demonstrates that combining two distinct small language mod…
cs.CL2024
SLM as Guardian: Pioneering AI Safety with Small Language Models
Ohjoon Kwon, Donghyeon Jeon, Nayoung Choi +6
Most prior safety research of large language models (LLMs) has focused on enhancing the alignment of LLMs to better suit the safety requirements of humans. However, internalizing s…