12 citations · 20 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…