Papers by Taehwan Kim
Data Descriptions from Large Language Models with Influence Estimation (2025.emnlp-main)
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| Challenge: | Existing explainable AI approaches focus on interpreting how models make predictions. |
| Approach: | They propose a pipeline that generates textual descriptions using large language models . they propose 'cross-modal transfer classification' task to examine effectiveness of textual description . |
| Outcome: | The proposed method improves classification accuracy compared to baselines and sheds light on how the model prioritizes and utilizes information for decision-making. |
Sound of Story: Multi-modal Storytelling with Audio (2023.findings-emnlp)
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| Challenge: | Existing studies on storytelling with sound have focused on visuals and sounds, but little attention has been given to sound. |
| Approach: | They propose to establish a new component called background sound which is story context-based audio without any linguistic information. |
| Outcome: | The proposed dataset is the largest well-curated dataset for storytelling with sound . it contains 27,354 stories with 19.6 images per story and 984 hours of speech-decoupled audio . |
VEHME: A Vision-Language Model For Evaluating Handwritten Mathematics Expressions (2025.emnlp-main)
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| Challenge: | VEHME is a vision language model for assessing handwritten math answers . traditional methods of assessing student work are limited by time constraints, class sizes and cognitive load . |
| Approach: | They propose a Vision-Language Model for Evaluating Handwritten Mathematics Expressions to assess handwritten math responses with high accuracy and interpretable reasoning traces. |
| Outcome: | VEHME achieves state-of-the-art performance among open-source models and approaches accuracy of proprietary systems. |
GMoE: Global Mixture of Experts with Logit Propagation (2026.acl-long)
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| Challenge: | Sparse Mixture of Experts architectures retain large memory footprints and exhibit significant redundancy, both within and across layers. |
| Approach: | They propose a sparse mixture of experts architecture that uses global experts shared across all layers and adds a Local Expert per layer for layer-specific adaptation. |
| Outcome: | The proposed architecture reduces computational cost by activating only a subset of experts per token while maintaining strong performance. |
Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation (2023.findings-emnlp)
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Gunsoo Han, Daejin Jo, Daniel Nam, Eunseop Yoon, Taehwan Kwon, Seungeun Rho, Kyoung-Woon On, Chang Yoo, Sungwoong Kim
| Challenge: | Existing knowledge-grounded dialogue generation algorithms require annotated knowledge to generate a response grounded on the retrieved knowledge. |
| Approach: | They propose an efficient algorithm for latent variable modeling that leverages large amount of dialogue data. |
| Outcome: | The proposed algorithm outperforms the supervised learning algorithm on knowledge-grounded dialogue datasets while maintaining efficiency and scalability. |
Selective Token Generation for Few-shot Natural Language Generation (2022.coling-1)
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| Challenge: | Experimental results show that the proposed selective token generation algorithm outperforms the previous additive learning algorithms based on the PLMs. |
| Approach: | They propose an additive learning algorithm that selectively outputs language tokens between a task-general PLM and a specific adapter during training and inference. |
| Outcome: | The proposed algorithm outperforms existing methods on few-shot natural language generation tasks. |
RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals (2025.findings-emnlp)
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| Challenge: | Transformers have shown strong performance in processing sequential data, but their parameters are larger . a novel approach to reduce the model parameters while maintaining high performance is proposed . |
| Approach: | They propose a transformer-based model that processes input repeatedly in a circular, ring-like manner. |
| Outcome: | The proposed approach reduces model parameters while maintaining high performance . the proposed approach is validated in the experiments. |