3 papers
cs.CL2025
Self-Correcting Code Generation Using Small Language Models
Jeonghun Cho, Deokhyung Kang, Hyounghun Kim +1
Self-correction has demonstrated potential in code generation by allowing language models to revise and improve their outputs through successive refinement. Recent studies have exp…
cs.CL2025
K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor
Jeonghun Cho, Gary Geunbae Lee
Retrieval-augmented question answering (QA) integrates external information and thereby increases the QA accuracy of reader models that lack domain knowledge. However, documents re…
cs.CL2025
Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation
Deokhyung Kang, Jeonghun Cho, Yejin Jeon +4
Visual programming languages (VPLs) allow users to create programs through graphical interfaces, which results in easier accessibility and their widespread usage in various domains…