12 citations · 20 across the 11 of their papers we have counts for
11 papers
Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code
Hyungjoo Chae, Taeyoon Kwon, Seungjun Moon +7
This paper presents Coffee-Gym, a comprehensive RL environment for training models that provide feedback on code editing. Coffee-Gym includes two major components: (1) Coffee, a da…
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Seungjun Moon, Hyungjoo Chae, Yongho Song +5
Code editing is an essential step towards reliable program synthesis to automatically correct critical errors generated from code LLMs. Recent studies have demonstrated that closed…
Evidence-empowered Transfer Learning for Alzheimer's Disease
Kai Tzu-iunn Ong, Hana Kim, Minjin Kim +6
Transfer learning has been widely utilized to mitigate the data scarcity problem in the field of Alzheimer's disease (AD). Conventional transfer learning relies on re-using models…
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
Seungone Kim, Se June Joo, Yul Jang +2
Chain-of-thought (CoT) prompting enables large language models (LLMs) to solve complex reasoning tasks by generating an explanation before the final prediction. Despite it's promis…
TUTORING: Instruction-Grounded Conversational Agent for Language Learners
Hyungjoo Chae, Minjin Kim, Chaehyeong Kim +4
In this paper, we propose Tutoring bot, a generative chatbot trained on a large scale of tutor-student conversations for English-language learning. To mimic a human tutor's behavio…
BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets
Minju Kim, Chaehyeong Kim, Yongho Song +2
To build open-domain chatbots that are able to use diverse communicative skills, we propose a novel framework BotsTalk, where multiple agents grounded to the specific target skills…
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
Seungone Kim, Se June Joo, Hyungjoo Chae +3
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We…
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…
Dual Task Framework for Improving Persona-grounded Dialogue Dataset
Minju Kim, Beong-woo Kwak, Youngwook Kim +3
This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents. Prior model-centric approaches unquestioningly dep…
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…
Meta-path Free Semi-supervised Learning for Heterogeneous Networks
Shin-woo Park, Byung Jun Bae, Jinyoung Yeo +1
Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved superior performance in tasks such as node classification. However, analyzing h…