Papers by Hyun Kim
SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter (2026.acl-long)
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| Challenge: | Existing approaches to understanding laughter or humor focus on narrowly defined tasks such as detecting humor and estimating humor intensity. |
| Approach: | They propose a dataset for real-world laughter understanding with multimodal textual representations and question–answer annotations. |
| Outcome: | The proposed framework outperforms baselines in three laughter-related tasks, showing that it is robust. |
SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models (2024.findings-naacl)
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| Challenge: | Despite advances in artificial intelligence, building social intelligence remains a challenge. |
| Approach: | They propose a task to explain why people laugh in a video and a dataset to do this. |
| Outcome: | The proposed dataset generates plausible explanations for laughter in video and in-the-wild videos. |
Emosical: An Emotion-Annotated Musical Theatre Dataset (2024.findings-emnlp)
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| Challenge: | Emosical provides rich emotion annotations for musical films by inferring the background story of the characters. |
| Approach: | They propose to use a multimodal dataset of musical films to generate annotated emotion tags for each sample by inferring the background story of the characters. |
| Outcome: | The proposed dataset provides rich emotion annotations for musical films by inferring the background story of the characters. |
Korean-Specific Dataset for Table Question Answering (2022.lrec-1)
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| Challenge: | Existing question answering systems mainly focus on text data, but few Korean datasets exist . a dataset for table question answering is written in English, but it lacks Korean-specific datasets . |
| Approach: | They construct Korean-specific datasets for table question answering using crowd-sourced workers . they then fine-tune the model with these datasets and report the evaluation results . |
| Outcome: | The proposed model is based on Korean datasets and is publicly available . the model is evaluated against other datasets from Korean question answering systems . |
Retrieval Enhanced Feedback via In-context Neural Error-book (2025.emnlp-main)
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| Challenge: | Existing methods for learning from errors lack a structured framework for analyzing and mitigating errors, especially in Multimodal Large Language Models (MLLMs). |
| Approach: | They propose a teacher-student framework that systematically structures errors to deliver targeted feedback for multimodal reasoning. |
| Outcome: | The proposed framework improves inference efficiency, token usage, and scalability by building a query-based structure that prioritizes visual information, diagnoses failure points, and guides corrective actions. |
ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent (2023.acl-long)
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| Challenge: | Existing methods for meeting summarization use extract-thengenerate method to select "salient" contents . extract-thangenerates method typically selects "selected" content in a distantly supervised manner . |
| Approach: | They propose a novel extractor-guided method to generate a summary from evidence sentences that "explain" a meeting summary. |
| Outcome: | The proposed method outperforms existing methods with gains of up to 3.13 in the ROUGE-1 score. |
GenPTQ: Green Post-Training Quantization for Large-Scale ASR Models with Mixed-Precision Bit Allocation (2025.findings-emnlp)
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| Challenge: | Large-scale models with high memory and computation demands face significant challenges for deployment. |
| Approach: | They propose a weight-only quantization method that optimizes for weights and QAT to restore accuracy. |
| Outcome: | The proposed method achieves 89.1% model size reduction with only 0.8% increase in WER and completes optimization in just 15 seconds. |