{"ID":2832112,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06991","arxiv_id":"2512.06991","title":"Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models","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 PICEPR (Psychology-Informed Contents Embeddings for Personality Recognition), featuring two pipelines: (a) Contents and (b) 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 \\textit{gpt4o} from OpenAI and \\textit{gemini} from Google, along with open-source models like \\textit{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.","short_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 PICEPR (Psychology-Informed Contents Embeddings for Personality Recognition), featuring two pipelines: (a) Contents and (b)...","url_abs":"https://arxiv.org/abs/2512.06991","url_pdf":"https://arxiv.org/pdf/2512.06991v1","authors":"[\"Jing Jie Tan\",\"Ban-Hoe Kwan\",\"Danny Wee-Kiat Ng\",\"Yan-Chai Hum\",\"Anissa Mokraoui\",\"Shih-Yu Lo\"]","published":"2025-12-07T20:52:00Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","project_urls":"[\"https://research.jingjietan.com/?q=PICEPR\"]","has_code":false}