arXiv AI By Sina Heydari, Majid Ramezani

HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction

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HyperPersona is a multi‑level hypergraph framework that models the hierarchical structure of text—document, sentence, and word—using hyperedges for documents and sentences and nodes for words. It employs a transformer‑based graph encoder to capture interactions across these linguistic layers, producing context‑sensitive, structurally grounded features for automatic personality prediction. Experiments on the Big Five personality dimensions demonstrate that HyperPersona, relying solely on text, outperforms state‑of‑the‑art baselines by effectively integrating multi‑level linguistic cues.

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arXiv AI
Aug 19

Beyond BFI: The CSI for Enhanced Reliability and Validity in Evaluating LLM Personality Traits

The paper introduces the Core Sentiment Inventory (CSI), a new personality trait evaluation tool for large language models (LLMs) that addresses reliability and validity issues found in existing methods like the Big Five Inventory (BFI). CSI is designed specifically for LLMs, supports both English and Chinese, and provides detailed psychological portraits of model behavior. Experiments show that CSI captures nuanced behavioral patterns, improves reliability, and correlates strongly (above 0.85) with real-world LLM outputs.

By Huanhuan Ma, Haisong Gong, Xiaoyuan Yi, Xing Xie, Philip S. Yu, Dongkuan Xu