Papers by KiJung Seo
Revisiting the Impact of Pursuing Modularity for Code Generation (2024.findings-emnlp)
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| Challenge: | a recent study examines the impact of modularity on code generation in large language models . modularity is not a core factor for improving performance of code generation models, argues a new study . |
| Approach: | They introduce a new metric to measure the impact of modularity in code generation . they find modularity is not a core factor for improving performance of LLMs . |
| Outcome: | The proposed metric shows that modularity is not a core factor for improving performance . coding assistants are becoming increasingly essential for programmers . |
ADVICE: Answer-Dependent Verbalized Confidence Estimation (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have enabled them to communicate their confidence in natural language, improving transparency and reliability. |
| Approach: | They propose a framework that promotes answer-grounded confidence estimation and analyze the dynamics of verbalized confidence estimation. |
| Outcome: | The proposed framework significantly improves confidence calibration while exhibiting strong generalization to unseen settings without degrading task performance. |