AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models. - Se Jung Kwon; Jeonghoon Kim; Jeongin Bae; Kang Min Yoo; Jin-Hwa Kim; Baeseong Park; Byeongwook Kim; Jung-Woo Ha 001; Nako Sung; Dongsoo Lee (EMNLP (Findings), 2022)

No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization. - June Yong Yang; Byeongwook Kim; Jeongin Bae; Beomseok Kwon; Gunho Park; Eunho Yang; Se Jung Kwon; Dongsoo Lee (CoRR, 2024)

To FP8 and Back Again: Quantifying the Effects of Reducing Precision on LLM Training Stability. - Joonhyung Lee; Jeongin Bae; Byeongwook Kim; Se Jung Kwon; Dongsoo Lee (CoRR, 2024)

Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models. - Seungcheol Park; Jeongin Bae; Beomseok Kwon; Minjun Kim 010; Byeongwook Kim; Se Jung Kwon; U Kang; Dongsoo Lee (ACL (1), 2025)

FIGLUT: An Energy-Efficient Accelerator Design for FP-INT GEMM Using Look-Up Tables. - Gunho Park; Hyeokjun Kwon; Jiwoo Kim; Jeongin Bae; Baeseong Park; Dongsoo Lee; Youngjoo Lee (HPCA, 2025)