Papers by Jiseon Kim
Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions (2026.findings-acl)
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| Challenge: | Human moral judgment is context-dependent and changes based on interpersonal relationships. |
| Approach: | They characterize LLM behavior using the Whistleblower’s Dilemma . they find moral rightness remains consistently fairness-oriented . |
| Outcome: | The model decisions mirror moral rightness judgments, rather than their behavioral predictions. |
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. |
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning (2021.emnlp-main)
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| Challenge: | Recent studies describe how to apply contrastive learning to the language domain but it is difficult to apply data augmentation methods directly to language modeling. |
| Approach: | They propose a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. |
| Outcome: | The proposed method outperforms baseline models on sentence-level tasks with only 70% of memory compared to the baseline model. |
Learning Bill Similarity with Annotated and Augmented Corpora of Bills (2021.emnlp-main)
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| Challenge: | despite the importance of bill-to-bill linkages, existing approaches fail to address semantic similarities across bills. |
| Approach: | They propose a 5-class classification task that closely reflects the nature of the bill generation process. |
| Outcome: | The proposed method captures similarities across legal documents at various levels of aggregation. |
Uncovering Factor-Level Preference to Improve Human-Model Alignment (2025.findings-emnlp)
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| Challenge: | Large language models exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. |
| Approach: | They propose a framework to uncover and measure factor-level preference alignment of humans and large language models (LLMs) |
| Outcome: | The proposed framework uncovers and measures factor-level preference alignment of humans and large language models. |
Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models (2024.emnlp-main)
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| Challenge: | While theory of mind (ToM) is naturally developed for humans in childhood, large language models (LLMs) exhibit inconsistency in ToM tasks, despite early reports of successful cases. |
| Approach: | They propose to evaluate human ToM precursors-perception inference and perception-to-belief inference-in large language models (LLMs) by annotating characters’ perceptions on ToMi and FANToM. |
| Outcome: | The proposed method significantly improves LLMs’ performance in false belief scenarios. |