大语言模型作为人格识别中原型网络的理论无关自适应度量对齐的评判器
Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition
AI总结：
该研究针对人格识别受理论依赖公式限制的问题，提出理论无关的JAM框架，通过注意力池化图原型网络和跨理论协调方法学习结构化表示并统一数据集，还用大语言模型作为评判器机制，提升了跨框架泛化能力和性能。
摘要：
传统上，人格识别受限于依赖理论的公式，模型训练是为了拟合预定义的心理分类法，而非揭示潜在的行为结构。这限制了泛化能力，因为人格本身应理解为理论不变的，而现有注释仅反映了同一潜在特征的部分且有时不一致的观点。在这项工作中，我们引入了JAM（自适应度量对齐评判器），这是一个理论无关的框架，将学习从适应预定义人格理论转向发现统一的潜在伪方面，以捕捉共享心理结构。该框架通过注意力池化图原型网络在嵌入空间中聚类学习结构化表示，并结合跨理论协调（CTH）方法，该方法整合了人类引导的联系和机器诱导的共识，以统一异构数据集而无需依赖预定义标签。为进一步提高鲁棒性和数据质量，我们纳入了大语言模型作为评判器机制，有两种配置，即循环前大语言模型和循环中大语言模型，用于识别模糊样本以指导自适应度量学习。实验表明，JAM提高了跨框架泛化能力和性能，朝着理论无关的人格推理迈出了坚实一步，并支持低资源人格理论。相关代码库、模型权重和工件可在该https网址获取。
英文摘要：
Personality recognition has traditionally been constrained by theory-dependent formulations, where models are trained to fit predefined psychological taxonomies rather than uncovering shared underlying behavioral structure. This limits generalization, as personality itself is better understood as theory-invariant, while existing annotations reflect only partial and sometimes inconsistent views of the same latent traits. In this work, we introduce JAM ((J)udge for (A)daptive (M)etric-Alignment), a theory-agnostic framework that shifts learning from adapting to predefined personality theories toward discovering unified latent pseudo-facets that capture shared psychological structure. Rather than constraining the model to any personality taxonomy during training or inference, the framework learns generalizable psychological representations and can infer an individual's latent psychological profile directly from the textual samples, without requiring theory-specific labels. JAM achieves this through an Attention-Pooled Graph Prototypical Network that learns structured representations via clustering in embedding space, together with a Cross-Theory Harmonization (CTH) approach that integrates (i) Human-Guided Linkage and (ii) Machine-Induced Consensus to unify heterogeneous datasets without relying on predefined labels. To further improve robustness and data quality, we incorporate an LLM-as-a-Judge mechanism operating in two configurations, (i) LLM-before-the-loop and (ii) LLM-in-the-loop which identifies ambiguous samples to guide adaptive metric learning. Experiments show that JAM improves cross-framework generalization and performance, establishing a strong step toward theory-agnostic personality inference and supporting low-resource personality theories. The related code repository, model weights, and artifacts are available at https://research.jingjietan.com/JAM
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
