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

2024-08-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

The JAM (Judge for Adaptive Metric-Alignment) framework introduces a theory-agnostic approach to personality recognition, moving beyond models constrained by predefined psychological taxonomies. It utilizes an Attention-Pooled Graph Prototypical Network to discover unified latent "pseudo-facets" from textual samples, integrating heterogeneous datasets without theory-specific labels. A key component is Cross-Theory Harmonization (CTH), combining Human-Guided Linkage and Machine-Induced Consensus. Furthermore, JAM incorporates an LLM-as-a-Judge (LAJ) mechanism, operating in "LLM-before-the-loop" or "LLM-in-the-loop" configurations, to identify ambiguous or mislabeled samples and guide adaptive metric learning. Experiments on Essays and Kaggle personality datasets show JAM improves cross-framework generalization and performance, achieving an average Balanced Accuracy improvement of 12% on Essays and 14% on Kaggle compared to regular prototypical few-shot learning. The LAJ mechanism further boosts Essays dataset performance by 2.4%.

Key takeaway

For AI Scientists and Machine Learning Engineers developing personality recognition systems, consider adopting theory-agnostic frameworks like JAM to overcome limitations of theory-dependent models and scarce annotated data. Your systems can achieve better cross-framework generalization and robustness by integrating heterogeneous datasets and leveraging LLMs for data quality assessment. Prioritize "LLM-before-the-loop" data refinement for faster convergence and improved performance, especially in low-resource scenarios.

Key insights

JAM enables theory-agnostic personality recognition by learning unified latent "pseudo-facets" across diverse psychological frameworks.

Principles

• Personality is theory-invariant, emerging from stable psychological patterns.
• Integrating heterogeneous datasets improves generalization.
• LLMs are effective as auxiliary data quality evaluators.

Method

JAM uses an Attention-Pooled Graph Prototypical Network for structured representation learning, Cross-Theory Harmonization (Human-Guided Linkage + Machine-Induced Consensus) for dataset integration, and an LLM-as-a-Judge to refine data quality.

In practice

• Employ LLMs to pre-filter noisy training data for better model convergence.
• Combine human-guided and machine-induced consensus for cross-dataset alignment.
• Use prototypical networks for robust learning with imbalanced data.

Topics

• Personality Recognition
• Theory-Agnostic Models
• Prototypical Networks
• Large Language Models
• Cross-Theory Harmonization
• Graph Neural Networks
• Data Quality Assessment

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition — JAM enables theory-agnostic personality recognition by learning unified latent psychological structures directly from text, guided by LLMs.
• Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition — A theory-agnostic framework uses LLMs and graph networks to discover unified latent personality traits from text.
• Domain-Adaptive Model Merging Across Disconnected Modes — DMM merges divergent models data-free by synthesizing pseudo-data and distilling knowledge, outperforming existing methods.
• Large Tabular Models Excel Where LLMs Fail — Large Tabular Models (LTMs) are purpose-built foundation models that excel at analyzing structured data where LLMs fail.
• An Interpretable and Scalable Framework for Evaluating Large Language Models — cBMM offers a scalable, interpretable framework for LLM evaluation by modeling latent abilities and item characteristics.
• LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction — LLMs serve best as complementary components to strong tabular machine learning baselines, not replacements, for industrial data with limited semantics.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.