# Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings

arXiv: https://arxiv.org/abs/2610.16050 · HTML: https://mlsanity.com/paper/2610.16050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 21 of 21)

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- [Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation](https://mlsanity.com/paper/2306.01183/) (2023)
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- [Is ChatGPT a Good Personality Recognizer? A Preliminary Study](https://mlsanity.com/paper/2307.03952/) (2023)
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- [Less but Better: Parameter-Efficient Fine-Tuning of Large Language Models for Personality Detection](https://mlsanity.com/paper/2504.05411/) (2025)
- [DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.17212/) (2026)
- [Personality Analysis from Online Short Video Platforms with Multi-domain Adaptation](https://mlsanity.com/paper/2411.00813/) (2024)
- [LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion](https://mlsanity.com/paper/2606.29900/) (2026)

## Abstract (arXiv)

Personality assessment from asynchronous video interviews (AVIs) plays a crucial role in recruitment by enabling standardized and behaviorally grounded evaluation for job applicants. However, most of the previous works are confined to a single interview protocol. Independently collected protocols differ in question sets and question-trait mapping. Thus, the human-rated evidence from one protocol cannot be used directly under another. To address this limitation, we propose the Cross-protocol Evidence-grounded Personality Assessment (CEPA) framework, which aligns behavioral representations across interview protocols. CEPA addresses protocol heterogeneity by projecting source behavioral records into the target protocol's construct-oriented representation space. It retrieves behaviorally relevant and personality-complementary human-rated exemplars to ground an ensemble of LLM raters, which enables behavior-to-personality associations without target-specific supervision. In cross-protocol experiments, CEPA reduces average mean squared error (MSE) by 43.8% (Forward) and 17.0% (Reverse) relative to the strongest baseline. Our empirical experiments show that human-rated evidence from one protocol can support personality assessment under another without target-specific supervision or retraining.