Papers by Changwoo Chun
To Chat or Task: a Multi-turn Dialogue Generation Framework for Task-Oriented Dialogue Systems (2025.acl-industry)
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| Challenge: | Large language models (LLMs) are designed to handle complex task requests, but lack of specific datasets for training and evaluation of such systems . |
| Approach: | They propose a framework to generate a dataset for in-vehicle speech recognition systems . they train an in-car context sensor that correctly identifies the functional intent of the driver . |
| Outcome: | The proposed framework outperforms baseline models across experimental settings. |
CReTIHC: Designing Causal Reasoning Tasks about Temporal Interventions and Hallucinated Confoundings (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in natural language processing, but their ability to establish causal relationships remains challenging. |
| Approach: | They propose a novel dataset to test and enhance the causal reasoning abilities of large language models (LLMs) by integrating elements of verbal hallucinations and temporal interventions into existing causal inference datasets. |
| Outcome: | The proposed dataset is designed to test and enhance the causal reasoning abilities of large language models. |
LLM ContextBridge: A Hybrid Approach for Intent and Dialogue Understanding in IVSR (2025.coling-industry)
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| Challenge: | In-vehicle speech recognition systems struggle with interpreting user intent accurately due to limitations in contextual understanding and ambiguity resolution. |
| Approach: | They propose a hybrid architecture that integrates Pretrained Language Model-based intent classification with Large Language Models to enhance both command recognition and dialogue management. |
| Outcome: | The proposed architecture improves recognition accuracy and user experience in multi-turn dialogues. |
EASE: Entity-Aware Sub-table Generation for Real-world Multi-table QA (2026.acl-long)
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Myunghoon Kang, Dahyun Jung, Suhyune Son, Seonmin Koo, Changwoo Chun, Daniel Rim, Haeyoung Kwon, Yuna Hur, Heuiseok Lim
| Challenge: | Table-based question answering (table QA) is a powerful tool for analyzing large language models. |
| Approach: | They propose to use noisy multi-table sets to generate sub-tables for table-based question answering. |
| Outcome: | The proposed framework efficiently filters out irrelevant information while incorporating pertinent table values. |
Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks (2023.findings-acl)
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Sugyeong Eo, Hyeonseok Moon, Jinsung Kim, Yuna Hur, Jeongwook Kim, SongEun Lee, Changwoo Chun, Sungsoo Park, Heuiseok Lim
| Challenge: | Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. |
| Approach: | They propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by significant margins, achieving improved diversity and quality. |