# LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection

arXiv: https://arxiv.org/abs/2504.02146 · HTML: https://mlsanity.com/paper/2504.02146/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection](https://mlsanity.com/paper/2310.20256/) (2023)
- [LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model](https://mlsanity.com/paper/2403.07581/) (2024)
- [Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models](https://mlsanity.com/paper/1907.06333/) (2019)
- [Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection](https://mlsanity.com/paper/2212.01515/) (2022)
- [Psycholinguistic Tripartite Graph Network for Personality Detection](https://mlsanity.com/paper/2106.04963/) (2021)
- [GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection](https://mlsanity.com/paper/2412.12164/) (2024)
- [Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives](https://mlsanity.com/paper/2404.15067/) (2024)

## Built on by (most cited first, 5 of 5)

- [Less but Better: Parameter-Efficient Fine-Tuning of Large Language Models for Personality Detection](https://mlsanity.com/paper/2504.05411/) (2025)
- [EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling](https://mlsanity.com/paper/2509.02450/) (2025)
- [PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation](https://mlsanity.com/paper/2512.19933/) (2025)
- [CALM: Culturally Self-Aware Language Models](https://mlsanity.com/paper/2601.03483/) (2026)
- [HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection](https://mlsanity.com/paper/2510.09893/) (2025)

## Abstract (arXiv)

Graph-based personality detection constructs graph structures from textual data, particularly social media posts. Current methods often struggle with sparse or noisy data and rely on static graphs, limiting their ability to capture dynamic changes between nodes and relationships. This paper introduces LL4G, a self-supervised framework leveraging large language models (LLMs) to optimize graph neural networks (GNNs). LLMs extract rich semantic features to generate node representations and to infer explicit and implicit relationships. The graph structure adaptively adds nodes and edges based on input data, continuously optimizing itself. The GNN then uses these optimized representations for joint training on node reconstruction, edge prediction, and contrastive learning tasks. This integration of semantic and structural information generates robust personality profiles. Experimental results on Kaggle and Pandora datasets show LL4G outperforms state-of-the-art models.