12 citations · 22 across the 12 of their papers we have counts for
12 papers
Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety
Seongmin Lee, Aeree Cho, Grace C. Kim +3
As large language models (LLMs) see wider real-world use, understanding and mitigating their unsafe behaviors is critical. Interpretation techniques can reveal causes of unsafe out…
Shape it Up! Restoring LLM Safety during Finetuning
ShengYun Peng, Pin-Yu Chen, Jianfeng Chi +2
Finetuning large language models (LLMs) enables user-specific customization but introduces critical safety risks: even a few harmful examples can compromise safety alignment. A com…
Effective Guidance for Model Attention with Simple Yes-no Annotations
Seongmin Lee, Ali Payani, Duen Horng Chau
Modern deep learning models often make predictions by focusing on irrelevant areas, leading to biased performance and limited generalization. Existing methods aimed at rectifying m…
Probing LLM Hallucination from Within: Perturbation-Driven Approach via Internal Knowledge
Seongmin Lee, Hsiang Hsu, Chun-Fu Chen +1
LLM hallucination, where unfaithful text is generated, presents a critical challenge for LLMs' practical applications. Current detection methods often resort to external knowledge,…
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining
ShengYun Peng, Aishwarya Chakravarthy, Seongmin Lee +3
Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse. Prior work on table recognition (TR) has m…
Self-Supervised Pre-Training for Table Structure Recognition Transformer
ShengYun Peng, Seongmin Lee, Xiaojing Wang +2
Table structure recognition (TSR) aims to convert tabular images into a machine-readable format. Although hybrid convolutional neural network (CNN)-transformer architecture is wide…
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models
Justin Blalock, David Munechika, Harsha Karanth +4
The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes. Traditional try-on methods grapple with chal…
High-Performance Transformers for Table Structure Recognition Need Early Convolutions
ShengYun Peng, Seongmin Lee, Xiaojing Wang +2
Table structure recognition (TSR) aims to convert tabular images into a machine-readable format, where a visual encoder extracts image features and a textual decoder generates tabl…
VisGrader: Automatic Grading of D3 Visualizations
Matthew Hull, Vivian Pednekar, Hannah Murray +9
Manually grading D3 data visualizations is a challenging endeavor, and is especially difficult for large classes with hundreds of students. Grading an interactive visualization req…
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
Seongmin Lee, Benjamin Hoover, Hendrik Strobelt +7
Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex structures and operations often pose challen…
SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks
Zijie J. Wang, David Munechika, Seongmin Lee +1
Computational notebooks, such as Jupyter Notebook, have become data scientists' de facto programming environments. Many visualization researchers and practitioners have developed i…
Explaining Website Reliability by Visualizing Hyperlink Connectivity
Seongmin Lee, Sadia Afroz, Haekyu Park +5
As the information on the Internet continues growing exponentially, understanding and assessing the reliability of a website is becoming increasingly important. Misinformation has…