Yonsei University - DLI LabI'm an Integrated M.S./Ph.D. student at Yonsei University Data & Language Intelligence Lab, advised by Prof. Dongha Lee.
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Yonsei UniversityDepartment of Artificial Intelligence
M.S./Ph.D. StudentMar. 2025 - present -
Yonsei UniversityB.S. in Computer Science and EngineeringMar. 2020 - Feb. 2025
Honors & Awards
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Honors Award1st semester, 2021
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Honors Award2nd semester, 2020
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Highest Honors Award1st semester, 2020
Selected Publications (view all )
MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tables
Donguk Kwon*, Kwangwook Seo*, Dongha Lee# (* equal contribution, # corresponding author)
Preprint (arXiv) 2025
Recent advancements in table-based reasoning have expanded beyond factoid-level QA to address insight-level tasks, where systems should synthesize implicit knowledge in the table to provide explainable analyses. Although effective, existing studies remain confined to scenarios where a single gold table is given alongside the user query, failing to address cases where users seek comprehensive insights from multiple unknown tables. To bridge these gaps, we propose MT-RAIG Bench, design to evaluate systems on Retrieval-Augmented Insight Generation over Mulitple-Tables. Additionally, to tackle the suboptimality of existing automatic evaluation methods in the table domain, we further introduce a fine-grained evaluation framework MT-RAIG Eval, which achieves better alignment with human quality judgments on the generated insights. We conduct extensive experiments and reveal that even frontier LLMs still struggle with complex multi-table reasoning, establishing our MT-RAIG Bench as a challenging testbed for future research.
MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tables
Donguk Kwon*, Kwangwook Seo*, Dongha Lee# (* equal contribution, # corresponding author)
Preprint (arXiv) 2025
Recent advancements in table-based reasoning have expanded beyond factoid-level QA to address insight-level tasks, where systems should synthesize implicit knowledge in the table to provide explainable analyses. Although effective, existing studies remain confined to scenarios where a single gold table is given alongside the user query, failing to address cases where users seek comprehensive insights from multiple unknown tables. To bridge these gaps, we propose MT-RAIG Bench, design to evaluate systems on Retrieval-Augmented Insight Generation over Mulitple-Tables. Additionally, to tackle the suboptimality of existing automatic evaluation methods in the table domain, we further introduce a fine-grained evaluation framework MT-RAIG Eval, which achieves better alignment with human quality judgments on the generated insights. We conduct extensive experiments and reveal that even frontier LLMs still struggle with complex multi-table reasoning, establishing our MT-RAIG Bench as a challenging testbed for future research.