1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
Adaptive Retrieval for Reasoning-Intensive Retrieval
Jongho Kim, Jaeyoung Kim, Seung-won Hwang +3
We study leveraging adaptive retrieval to ensure sufficient "bridge" documents are retrieved for reasoning-intensive retrieval. Bridge documents are those that contribute to the re…
SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety
Geon-Hyeong Kim, Yu Jin Kim, Byoungjip Kim +4
As Large Language Models (LLMs) are increasingly deployed in real-world applications, balancing helpfulness and safety has become a central challenge. A natural approach is to inco…
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning
Yu Jin Kim, Beong-woo Kwak, Youngwook Kim +3
Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to…
TrustAL: Trustworthy Active Learning using Knowledge Distillation
Beong-woo Kwak, Youngwook Kim, Yu Jin Kim +2
Active learning can be defined as iterations of data labeling, model training, and data acquisition, until sufficient labels are acquired. A traditional view of data acquisition is…