Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models

Jing Jie Tan, Ban-Hoe Kwan, Danny Wee-Kiat Ng, Yan Chai Hum, Anissa Mokraoui, Shih-Yu Lo

Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks. This research introduces a novel “Prompting-in-a-Series” algorithm, termed psychology-informed content embeddings for personality recognition (PICEPR), featuring two pipelines: 1) contents; and 2) embeddings. The approach demonstrates how a modularised decoder-only LLM can summarize or generate content, which can aid in classifying or enhancing personality recognition functions as a personality feature extractor and a generator for personality-rich content. We conducted various experiments to provide evidence to justify the rationale behind the PICEPR algorithm. Meanwhile, we also explored closed-source models such as gpt4o from OpenAI and gemini from Google, along with open-source models such as mistral from Mistral AI, to compare the quality of the generated content. The PICEPR algorithm has achieved a new state-of-the-art performance for personality recognition by 5–15% improvement. The work repository and models’ weight can be found at: https://research.jingjietan.com/?q=PICEPR.

External IDs:dblp:journals/tcss/TanKNHML26