Less but Better: Parameter-Efficient Fine-Tuning of Large Language Models for Personality Detection
This addresses the problem of managing computational expenses in personality detection for researchers and practitioners, but it is incremental as it builds on existing parameter-efficient fine-tuning methods.
The paper tackles the high computational cost of fine-tuning large language models for personality detection by introducing PersLLM, a parameter-efficient framework that reduces computational cost while maintaining competitive performance on benchmark datasets like Kaggle and Pandora.
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.