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PsyCoT: A Window into the Future of AI That Can Perceive Our Personalities and Moods

Personality detection involves identifying an individual's personality traits purely from their written text. This capability has many potential applications, like improving human-computer interaction. However, traditional machine learning models struggle to capture the nuances required for accurate personality detection from texts.

Researchers from Sun Yat-sen University propose a new method called PsyCoT that takes inspiration from psychological questionnaires to significantly boost the performance of large language models (LLMs) like GPT-3.5 for zero-shot personality detection.

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PsyCoT Approach - Image source: Yang et al.

The PsyCoT Approach

PsyCoT treats personality detection as a multi-step reasoning task, similar to a person completing a personality questionnaire. It has the LLM take on the role of an AI assistant specializing in text analysis.

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PsyCoT Multi-turn Dialogue - Image source: Yang et al.

Over multiple dialogue turns, the assistant is prompted to rate relevant questionnaire items based on the input text, with access to previous ratings. After rating all items, the assistant makes an overall personality judgment. This mimics the human process of reasoning through a questionnaire to evaluate someone's personality. The items act as an effective chain-of-thought, enhancing the LLM's capability for nuanced personality inference.

Key Takeaways

Overall, PsyCoT provides an effective way to incorporate rigorous human reasoning processes into LLMs for improved complex judgment capabilities.

Looking Ahead

By incorporating the rigorous reasoning process of questionnaires, PsyCoT unlocks stronger personality detection abilities in LLMs without any training. It provides a promising new prompting technique for complex human judgments.

Future work can explore PsyCoT's effectiveness for different LLMs, languages, and questionnaires. Overall, it offers an exciting way to elicit nuanced reasoning from the knowledge and capabilities of LLMs.

Full credit to Yang, Tao, Tianyuan Shi, Fanqi Wan, Xiaojun Quan, Qifan Wang, Bingzhe Wu, and Jiaxiang Wu. "PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection." arXiv preprint arXiv:2310.20256 (2023).

The paper is available on arXiv at this link: