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Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition

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large-language-models-as-a-judge-in-theory-agnostic-adaptive-metric-alignment-for-prototypical-networks-in-personality-recognition-7fbe0303

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Aliases: Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition

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Cross-Theory Harmonization JAM (Judge for Adaptive Metric-Alignment)

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4arXiv · cs.CL·3h ago·source ↗

JAM: LLM-as-a-Judge framework for theory-agnostic personality recognition via prototypical networks

Researchers introduce JAM (Judge for Adaptive Metric-Alignment), a framework for personality recognition that avoids dependence on predefined psychological taxonomies like Big Five or MBTI. The system uses an Attention-Pooled Graph Prototypical Network with a Cross-Theory Harmonization approach to unify heterogeneous personality datasets, and incorporates an LLM-as-a-Judge mechanism in two configurations to identify ambiguous training samples and guide metric learning. Experiments show improved cross-framework generalization, with the approach enabling inference of latent psychological profiles from text without theory-specific labels.

Evaluation and Benchmarking Cross-Theory Harmonization Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition JAM (Judge for Adaptive Metric-Alignment)