Papers by Seogyeong Jeong
LoCar: Localization-Aware Evaluation of In-Vehicle Assistants through Fine-Grained Sociolinguistic Control (2026.acl-industry)
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| Challenge: | Using Large Language Models (LLMs) is challenging due to lack of domain-specific evaluation standards . current LLMs prioritize reasoning or knowledge over sociolinguistic nuances vital for automotive settings . |
| Approach: | They propose a framework for evaluation of Korean-language in-vehicle assistants . they propose to evaluate fine-grained Korean honorific control and safetycritical response behavior . |
| Outcome: | The proposed evaluation framework evaluates fine-grained honorific control, safetycritical response behavior, and task efficiency in deployment-aligned settings. |
MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language (2025.findings-emnlp)
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| Challenge: | Evaluating text generation capabilities of large language models (LLMs) is challenging, especially for low-resource languages where methods for direct assessment are scarce. |
| Approach: | They propose a framework that transforms existing benchmarks into conversational tasks and measures LLMs’ accuracies on those tasks. |
| Outcome: | The proposed framework correlates strongly with established benchmarks while enabling standardized comparisons across languages and models. |