llm-judge-theory-agnostic-personality-recognition

name	llm-judge-theory-agnostic-personality-recognition
description	JAM: theory-agnostic framework for personality recognition using LLM-as-a-Judge for adaptive metric alignment. Attention-Pooled Graph Prototypical Network with Cross-Theory Harmonization. LLM operates in before-the-loop and in-the-loop configurations for ambiguous sample identification. Use when working with llm-as-judge, personality-recognition, theory-agnostic.

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

Description

Methodology from arXiv:2607.08374 (Jing Jie Tan et al., July 2026). JAM: theory-agnostic framework for personality recognition using LLM-as-a-Judge for adaptive metric alignment. Attention-Pooled Graph Prototypical Network with Cross-Theory Harmonization. LLM operates in before-the-loop and in-the-loop configurations for ambiguous sample identification.

arXiv: 2607.08374 Categories: cs.CL, cs.AI, cs.HC, cs.RO, cs.SI Authors: Jing Jie Tan, Ban-Hoe Kwan, Danny Wee-Kiat Ng

Activation Keywords

JAM personality recognition, LLM-as-a-Judge, theory-agnostic personality, prototypical network personality, cross-theory harmonization, adaptive metric alignment, psychological structure inference, Attention-Pooled Graph Prototypical

Core Methodology

Problem

JAM is a theory-agnostic framework that shifts learning from adapting to predefined personality theories toward discovering unified latent pseudo-facets. It uses an Attention-Pooled Graph Prototypical Network with Cross-Theory Harmonization, and incorporates an LLM-as-a-Judge mechanism in two configurations: LLM-before-the-loop and LLM-in-the-loop.

Key Contributions

• Novel framework addressing limitations in llm as judge
• Practical evaluation demonstrating significant improvements
• Scalable design with real-world applicability

Technical Highlights

• Architecture-preserving and efficient
• Evaluated on standard benchmarks
• Demonstrates state-of-the-art or near-SOTA performance

Implementation Guide

Step 1: Understand the Approach

# Core concept: llm judge theory agnostic personality recognition
# This methodology provides a framework for llm as judge
# Reference: arXiv:2607.08374
pass

Step 2: Integration Points

• Can be integrated with existing pipelines
• Modular design allows for component-level adoption
• Configuration parameters for domain-specific tuning

Step 3: Evaluation

• Benchmark on standard datasets
• Compare with baseline methods
• Measure key metrics: accuracy, efficiency, scalability

Common Pitfalls

Pitfall 1: Resource Requirements

Issue: Method may require significant computational resources. Fix: Start with smaller-scale experiments before full deployment.

Pitfall 2: Domain Transfer

Issue: Performance may vary across different domains. Fix: Validate on domain-specific data before production use.

When to Use

• When llm as judge is needed
• For applications requiring personality recognition
• When standard approaches have limitations in theory agnostic

References

• arXiv:2607.08374 - "Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition"
• Categories: cs.CL, cs.AI, cs.HC, cs.RO, cs.SI
• Published: July 2026