Papers by Woojin Kim
Do Large Language Models Have “Emotion Neurons”? Investigating the Existence and Role (2025.findings-acl)
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| Challenge: | Existing evaluations of LLMs' emotional capabilities have been criticized for not illuminating how emotion information is processed and represented within an LLM. |
| Approach: | They examine whether there are “emotion neurons” within large language models that selectively process and express certain emotions and what functional role they play. |
| Outcome: | The proposed model is based on the representative emotion theory of the six basic emotions and demonstrates that it is functionally significant to examine whether the prediction accuracy for a specific emotion decreases when the neurons are removed. |
Analyzing Key Factors Influencing Emotion Prediction Performance of VLLMs in Conversational Contexts (2024.emnlp-main)
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| Challenge: | Recent studies show that large language models and vision large language model (VLLMs) possess EI and the ability to understand emotional stimuli in the form of text and images. |
| Approach: | They analyze the key elements affecting the emotion prediction performance of VLLMs in conversational contexts. |
| Outcome: | The proposed model performance was compared with other models in a conversational context. |
Generalizing Clinical De-identification Models by Privacy-safe Data Augmentation using GPT-4 (2024.emnlp-main)
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| Challenge: | De-identification (de-ID) is critical for patient confidentiality in clinical data management due to the difficulty of retaining training corpora and labeling standards vary across institutions. |
| Approach: | They propose to exploit GPT-4 for data augmentation through one-shot and zero-shot prompts to exploit the problem of PHI leakage by redacting PHI before processing. |
| Outcome: | The proposed approach significantly improves on three types of F1 scores in cross-dataset testing. |