Si-Eun Park

Jemin Lee, Sihyeong Park, Jinse Kwon et al.

Quantization has gained attention as a promising solution for the cost-effective deployment of large and small language models. However, most prior work has been limited to perplexity or basic knowledge tasks and lacks a comprehensive evaluation of recent models like Llama-3.3. In this paper, we conduct a comprehensive evaluation of instruction-tuned models spanning 1B to 405B parameters, applying four quantization methods across 13 datasets. Our findings reveal that (1) quantized models generally surpass smaller FP16 baselines, yet they often struggle with instruction-following and hallucination detection; (2) FP8 consistently emerges as the most robust option across tasks, and AWQ tends to outperform GPTQ in weight-only quantization; (3) smaller models can suffer severe accuracy drops at 4-bit quantization, while 70B-scale models maintain stable performance; (4) notably, \textit{hard} tasks do not always experience the largest accuracy losses, indicating that quantization magnifies a model's inherent weaknesses rather than simply correlating with task difficulty; and (5) an LLM-based judge (MT-Bench) highlights significant performance declines in Coding and STEM tasks, though it occasionally reports improvements in reasoning.

Daeun Seo, Hoeseok Yang, Sihyeong Park et al.

Multi-scale image resolution is a de facto standard approach in modern object detectors, such as DETR. This technique allows for the acquisition of various scale information from multiple image resolutions. However, manual hyperparameter selection of the resolution can restrict its flexibility, which is informed by prior knowledge, necessitating human intervention. This work introduces a novel strategy for learnable resolution, called Elastic-DETR, enabling elastic utilization of multiple image resolutions. Our network provides an adaptive scale factor based on the content of the image with a compact scale prediction module (< 2 GFLOPs). The key aspect of our method lies in how to determine the resolution without prior knowledge. We present two loss functions derived from identified key components for resolution optimization: scale loss, which increases adaptiveness according to the image, and distribution loss, which determines the overall degree of scaling based on network performance. By leveraging the resolution's flexibility, we can demonstrate various models that exhibit varying trade-offs between accuracy and computational complexity. We empirically show that our scheme can unleash the potential of a wide spectrum of image resolutions without constraining flexibility. Our models on MS COCO establish a maximum accuracy gain of 3.5%p or 26% decrease in computation than MS-trained DN-DETR.