Papers by Jaeseok Kim
ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models (2024.acl-long)
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| Challenge: | Despite the critical role of software requirements, these criteria have not been studied actively in previous code generation works. |
| Approach: | They propose a framework that leverages in-context learning to organize and extrapolate unexpressed requirements from textual descriptions. |
| Outcome: | The proposed framework generates functional requirements from textual descriptions and extrapolates unexpressed requirements from them. |
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering (2024.findings-acl)
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ChaeHun Park, Koanho Lee, Hyesu Lim, Jaeseok Kim, Junmo Park, Yu-Jung Heo, Du-Seong Chang, Jaegul Choo
| Challenge: | Recent studies have employed machine translation systems for cross-lingual VQA tasks . however, translated texts contain unique characteristics distinct from human-written ones, referred to as translation artifacts. |
| Approach: | They propose a machine translation system that can train models in multiple languages . they propose augmentation strategies that reduce translation artifacts in translated texts . |
| Outcome: | The proposed approach reduces translation artifacts in models across languages and languages. |
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration (2025.acl-long)
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| Challenge: | Existing approaches to creating inclusive vision-language models rely on human annotators, making it labor-intensive and creating cognitive burdens. |
| Approach: | They propose a semi-automated framework for constructing cultural VLM benchmarks . they use an annotated sample of Korean culture to generate questions . |
| Outcome: | The proposed framework is based on a Korean culture dataset and shows that open-source models lag behind proprietary ones in understanding Korean culture. |
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation (2025.coling-main)
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| Challenge: | Using large language models to generate code has shown significant promise, but selecting effective examples to improve generation quality remains a challenging task. |
| Approach: | They propose a framework that utilizes algorithmic plans to identify and retrieve effective examples. |
| Outcome: | The proposed framework outperforms the state-of-the-art RAG methods in code generation even when the source and target languages match or differ. |