7 papers
LFQ: Logit-aware Final-block Quantization for Boosting the Generation Quality of Low-Bit Quantized LLMs
Jung Hyun Lee, June Yong Yang, Jungwook Choi +1
As large language models continue to scale, low-bit weight-only post-training quantization (PTQ) offers a practical solution to their memory-efficient deployment. Although block-wi…
A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective
Yeonsung Jung, Jaeyun Song, June Yong Yang +3
Learning generalized models from biased data is an important undertaking toward fairness in deep learning. To address this issue, recent studies attempt to identify and leverage bi…
Preserve or Modify? Context-Aware Evaluation for Balancing Preservation and Modification in Text-Guided Image Editing
Yoonjeon Kim, Soohyun Ryu, Yeonsung Jung +5
The development of vision-language and generative models has significantly advanced text-guided image editing, which seeks the preservation of core elements in the source image whi…
Token-Supervised Value Models for Enhancing Mathematical Problem-Solving Capabilities of Large Language Models
Jung Hyun Lee, June Yong Yang, Byeongho Heo +4
With the rapid advancement of test-time compute search strategies to improve the mathematical problem-solving capabilities of large language models (LLMs), the need for building ro…
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
Jung Hyun Lee, Jeonghoon Kim, June Yong Yang +4
With the commercialization of large language models (LLMs), weight-activation quantization has emerged to compress and accelerate LLMs, achieving high throughput while reducing inf…
AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler
Changhun Kim, Taewon Kim, Seungyeon Woo +2
In real-world scenarios, tabular data often suffer from distribution shifts that threaten the performance of machine learning models. Despite its prevalence and importance, handlin…