Papers by Seonghyeon Park
EpiCaR: Knowing What You Don’t Know Matters for Better Reasoning in LLMs (2026.acl-long)
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| Challenge: | Existing approaches to improving reasoning abilities of large language models incur a significant calibration cost. |
| Approach: | They propose an epistemic learning problem that integrates reasoning and calibration into an iterative supervised training framework. |
| Outcome: | The proposed method achieves Pareto-superiority over standard baselines in accuracy and calibration. |
Dimensional Emotion Detection from Categorical Emotion (2021.emnlp-main)
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| Challenge: | Using a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations, we show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorial emotion classification and shows significant positive correlations with the ground truth VAD scores. |
| Approach: | They propose to train a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance with a corpus with categorical emotion annotations. |
| Outcome: | The proposed model can predict emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |