5 papers
Source-Grounded Data Generation for Text-to-JSON Learning
Sunghee Ahn, Guijin Son, Youngjae Yu
From financial filings to clinical records, legacy industries rely heavily on long, unstructured documents to store high-value information. Reliably extracting this information int…
InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluation
Junhyeong Hwangbo, Soohyun Lee, Hyeon Jeon +4
Even LLMs that appear safe during evaluation can still produce harmful responses in deployment. Because stochastic sampling yields different responses to the same prompt, low-proba…
Self-Improving CAD Generation Agents with Finite Element Analysis as Feedback
Guijin Son, Jehyun Park, Seyeon Park +2
Computer-aided design (CAD) is the backbone of modern industrial design, yet learned CAD generators still fall short of real engineering pipelines: they neither iterate like engine…
A11YN: aligning LLMs for accessible web UI code generation
Janghan Yoon, Jaegwan Cho, Junhyeok Kim +3
Large language models (LLMs) have recently demonstrated strong capabilities in generating functional and aesthetic web interfaces directly from instructions. However, these models…
: Scalable Auto-Feedback for LLM-based Chart Generation
Woosung Koh, Jang Han Yoon, MinHyung Lee +7
Generating high-quality charts with Large Language Models (LLMs) presents significant challenges due to limited data and the high cost of scaling through human curation. $\langle \…