Papers by Gio Paik
HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition (2026.findings-eacl)
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| Challenge: | Recent advances in automatic speech recognition (ASR) have pushed error rates below 5% on standard monolingual benchmarks. |
| Approach: | They propose a framework for the evaluation of multilingual ASR models using loanword labels and a hierarchical CS-level labeling scheme that allows for fine-tuning with synthetic CS data. |
| Outcome: | The proposed framework provides a means for the precise evaluation of multilingual ASR models and fosters research in the field. |
MMRefine: Unveiling the Obstacles to Robust Refinement in Multimodal Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances have enabled MLLMs to tackle complex challenges such as mathematical reasoning and multimodal understanding. |
| Approach: | They propose a multimodal refinement benchmark to evaluate the refinement capabilities of Multimodal Large Language Models (MLLMs) the benchmark categorizes errors into six error types to highlight areas for improvement in effective reasoning enhancement. |
| Outcome: | The proposed framework evaluates the refinement capabilities of multimodal large language models across six scenarios. |