Prompting-in-a-Series: 基于心理启发内容与嵌入的个性识别解码器-only模型
Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models
AI总结：
PICEPR通过心理启发内容与嵌入技术，提升解码器-only模型在个性识别中的性能，实现5-15%的提升。
摘要：
大型语言模型（LLMs）在各种自然语言处理任务中展示了显著的能力。本研究介绍了一种新的
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
