# Less but Better: Parameter-Efficient Fine-Tuning of Large Language Models for Personality Detection

arXiv: https://arxiv.org/abs/2504.05411 · HTML: https://mlsanity.com/paper/2504.05411/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 22 of 22)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Towards a Unified View of Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2110.04366/) (2021)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification](https://mlsanity.com/paper/2303.08446/) (2023)
- [Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning](https://mlsanity.com/paper/2311.11077/) (2023)
- [Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights](https://mlsanity.com/paper/2007.00864/) (2020)
- [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)
- [HFT: Half Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2404.18466/) (2024)
- [Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection](https://mlsanity.com/paper/2212.01515/) (2022)
- [GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection](https://mlsanity.com/paper/2412.12164/) (2024)
- [LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2403.08822/) (2024)
- [Continuous Output Personality Detection Models via Mixed Strategy Training](https://mlsanity.com/paper/2406.16223/) (2024)
- [LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection](https://mlsanity.com/paper/2504.02146/) (2025)
- [IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification](https://mlsanity.com/paper/2401.04025/) (2024)
- [Personality-aware Human-centric Multimodal Reasoning: A New Task, Dataset and Baselines](https://mlsanity.com/paper/2304.02313/) (2023)

## Built on by (most cited first, 7 of 7)

- [EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling](https://mlsanity.com/paper/2509.02450/) (2025)
- [CALM: Culturally Self-Aware Language Models](https://mlsanity.com/paper/2601.03483/) (2026)
- [Dynamic Personality Adaptation in Large Language Models via State Machines](https://mlsanity.com/paper/2602.22157/) (2026)
- [HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection](https://mlsanity.com/paper/2510.09893/) (2025)
- [From Classification to Ranking: Enhancing LLM Reasoning Capabilities for MBTI Personality Detection](https://mlsanity.com/paper/2601.18582/) (2026)
- [RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit](https://mlsanity.com/paper/2606.06027/) (2026)
- [Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings](https://mlsanity.com/paper/2610.16050/) (2026)

## Abstract (arXiv)

Personality detection automatically identifies an individual's personality from various data sources, such as social media texts. However, as the parameter scale of language models continues to grow, the computational cost becomes increasingly difficult to manage. Fine-tuning also grows more complex, making it harder to justify the effort and reliably predict outcomes. We introduce a novel parameter-efficient fine-tuning framework, PersLLM, to address these challenges. In PersLLM, a large language model (LLM) extracts high-dimensional representations from raw data and stores them in a dynamic memory layer. PersLLM then updates the downstream layers with a replaceable output network, enabling flexible adaptation to various personality detection scenarios. By storing the features in the memory layer, we eliminate the need for repeated complex computations by the LLM. Meanwhile, the lightweight output network serves as a proxy for evaluating the overall effectiveness of the framework, improving the predictability of results. Experimental results on key benchmark datasets like Kaggle and Pandora show that PersLLM significantly reduces computational cost while maintaining competitive performance and strong adaptability.