3 papers
cs.CL2026
SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning
Daeyong Kwon, Soyoung Yoon, Seung-won Hwang
Multi-hop QA benchmarks often reward Large Language Models (LLMs) for spurious correctness, where models reach correct answers through invalid intermediate reasoning. We propose SA…
cs.SE2026
DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode
Hojae Han, Jaejin Kim, Seung-won Hwang +2
This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior approaches generate code first t…
cs.CL2025
Chain of Grounded Objectives: Bridging Process and Goal-oriented Prompting for Code Generation
Sangyeop Yeo, Seung-won Hwang, Yu-Seung Ma
The use of Large Language Models (LLMs) for code generation has gained significant attention in recent years. Existing methods often aim to improve the quality of generated code by…