Large language models (LLMs) are increasingly applied to automatic personality assessment, yet most prior work relies on coarse binary labels and direct domain-level predictions, limiting interpretability and ignoring the hierarchical facet structure of personality. In this study, we implement a structured prompting approach with three complementary objectives: direct domain-level prediction, fine-grained facet-level prediction, and domain-level prediction informed by facet outputs. All predictions use a five-level ordinal label scheme, capturing a continuum from very low to very high trait expression. Across all prompt types, we adopt an error-guided self-refinement procedure using in-context learning (ICL) to guide the model toward more accurate predictions. Zero-shot prompts assess baseline performance, while one-shot prompts incorporate a single demonstration example selected through the refinement procedure. Our framework evaluates both domain- and facet-level predictions, enabling examination of how prediction granularity and targeted exemplar selection influence LLM inference. By combining hierarchical domain-facet relationships with structured prompting and refinement, this work aims to provide a systematic approach for interpretable and principled LLM-based personality assessment from long-form life narratives.

Socioeconomic status (SES) reflects an individual’s standing in society, from a holistic set of factors including income, education level, and occupation. Identifying individuals in low-SES groups is crucial to ensuring they receive necessary support. However, many individuals may be hesitant to disclose their SES directly. This study introduces a federated learning-powered framework capable of verifying individuals’ SES levels through the analysis of their communications described in natural language. We propose to study language usage patterns among individuals from different SES groups using clustering and topic modeling techniques. An empirical study leveraging life narrative interviews demonstrates the effectiveness of our proposed approach.