# PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection

arXiv: https://arxiv.org/abs/2310.20256 · HTML: https://mlsanity.com/paper/2310.20256/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 10 of 10)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models](https://mlsanity.com/paper/2302.00618/) (2023)
- [Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation](https://mlsanity.com/paper/2306.01183/) (2023)

## Built on by (most cited first, 21 of 21)

- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs](https://mlsanity.com/paper/2404.15676/) (2024)
- [ESCoT: Towards Interpretable Emotional Support Dialogue Systems](https://mlsanity.com/paper/2406.10960/) (2024)
- [Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models](https://mlsanity.com/paper/2401.07115/) (2024)
- [The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models](https://mlsanity.com/paper/2406.11096/) (2024)
- [Self-assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models](https://mlsanity.com/paper/2406.17624/) (2024)
- [Large Language Models Can Infer Personality from Free-Form User Interactions](https://mlsanity.com/paper/2405.13052/) (2024)
- [EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection](https://mlsanity.com/paper/2406.16079/) (2024)
- [Less but Better: Parameter-Efficient Fine-Tuning of Large Language Models for Personality Detection](https://mlsanity.com/paper/2504.05411/) (2025)
- [Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives](https://mlsanity.com/paper/2404.15067/) (2024)
- [A Review of Incorporating Psychological Theories in LLMs](https://mlsanity.com/paper/2505.00003/) (2025)
- [LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection](https://mlsanity.com/paper/2504.02146/) (2025)
- [CAPE: Context-Aware Personality Evaluation Framework for Large Language Models](https://mlsanity.com/paper/2508.20385/) (2025)
- [EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling](https://mlsanity.com/paper/2509.02450/) (2025)
- [UniTransfer: Video Concept Transfer via Progressive Spatial and Timestep Decomposition](https://mlsanity.com/paper/2509.21086/) (2025)
- [LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion](https://mlsanity.com/paper/2606.29900/) (2026)
- [Measuring How LLMs Internalize Human Psychological Concepts: A preliminary analysis](https://mlsanity.com/paper/2506.23055/) (2025)
- [PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues](https://mlsanity.com/paper/2604.18354/) (2026)
- [MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling](https://mlsanity.com/paper/2606.29265/) (2026)
- [PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models](https://mlsanity.com/paper/2607.10245/) (2026)
- [LEX-EC: A Lexical Evidence-Channel Audit Framework for Zero-Shot LLM Personality Classification in Black-Box Settings](https://mlsanity.com/paper/2607.24435/) (2026)

## Abstract (arXiv)

Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in personality detection, which involves identifying an individual's personality from their written texts, remains largely unexplored. Drawing inspiration from Psychological Questionnaires, which are carefully designed by psychologists to evaluate individual personality traits through a series of targeted items, we argue that these items can be regarded as a collection of well-structured chain-of-thought (CoT) processes. By incorporating these processes, LLMs can enhance their capabilities to make more reasonable inferences on personality from textual input. In light of this, we propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. In particular, we employ a LLM as an AI assistant with a specialization in text analysis. We prompt the assistant to rate individual items at each turn and leverage the historical rating results to derive a conclusive personality preference. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection, achieving an average F1 score improvement of 4.23/10.63 points on two benchmark datasets compared to the standard prompting method. Our code is available at https://github.com/TaoYang225/PsyCoT.