Rank-O-ToM: Unlocking Emotional Nuance Ranking to Enhance Affective Theory-of-Mind
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909545951920128 |
|---|---|
| author | Kim, JiHyun Kwon, JuneHyoung Kim, MiHyeon Lee, Eunju Kim, YoungBin |
| author_facet | Kim, JiHyun Kwon, JuneHyoung Kim, MiHyeon Lee, Eunju Kim, YoungBin |
| contents | Facial Expression Recognition (FER) plays a foundational role in enabling AI systems to interpret emotional nuances, a critical aspect of affective Theory of Mind (ToM). However, existing models often struggle with poor calibration and a limited capacity to capture emotional intensity and complexity. To address this, we propose Ranking the Emotional Nuance for Theory of Mind (Rank-O-ToM), a framework that leverages ordinal ranking to align confidence levels with the emotional spectrum. By incorporating synthetic samples reflecting diverse affective complexities, Rank-O-ToM enhances the nuanced understanding of emotions, advancing AI's ability to reason about affective states. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16461 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rank-O-ToM: Unlocking Emotional Nuance Ranking to Enhance Affective Theory-of-Mind Kim, JiHyun Kwon, JuneHyoung Kim, MiHyeon Lee, Eunju Kim, YoungBin Human-Computer Interaction Artificial Intelligence Facial Expression Recognition (FER) plays a foundational role in enabling AI systems to interpret emotional nuances, a critical aspect of affective Theory of Mind (ToM). However, existing models often struggle with poor calibration and a limited capacity to capture emotional intensity and complexity. To address this, we propose Ranking the Emotional Nuance for Theory of Mind (Rank-O-ToM), a framework that leverages ordinal ranking to align confidence levels with the emotional spectrum. By incorporating synthetic samples reflecting diverse affective complexities, Rank-O-ToM enhances the nuanced understanding of emotions, advancing AI's ability to reason about affective states. |
| title | Rank-O-ToM: Unlocking Emotional Nuance Ranking to Enhance Affective Theory-of-Mind |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2503.16461 |