Researchers introduce JAM, a framework that shifts personality recognition from predefined psychological taxonomies to discovering unified latent pseudo-facets. This approach allows models to infer individual psychological profiles directly from text without relying on theory-specific labels.
- Utilizes an Attention-Pooled Graph Prototypical Network to learn structured representations via clustering in embedding space.
- Employs Cross-Theory Harmonization (CTH) with Human-Guided Linkage and Machine-Induced Consensus to unify heterogeneous datasets.
- Incorporates an LLM-as-a-Judge mechanism in two configurations to identify ambiguous samples and guide adaptive metric learning.
The method improves cross-framework generalization and performance, supporting low-resource personality theories.