用心理学启发的提示序列提升大模型识别人格能力
Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Models
- 设计双管道提示序列，融合心理学内容与嵌入生成
- 在人格识别任务上实现5%-15%性能提升
- 适配开源与闭源大模型，可作为人格特征提取器
大型语言模型在自然语言处理任务中表现出色。本文提出一种新型「提示序列化」算法PICEPR（心理学启发的内容与嵌入人格识别），包含内容与嵌入两条并行路径。该方法通过模块化解码器仅有的大模型，实现内容摘要或生成，可作为人格特征提取器或生成丰富人格内容的生成器。我们通过多项实验验证了PICEPR算法的有效性，并对比了OpenAI的gpt4o、Google的gemini以及Mistral AI的mistral等闭源与开源模型生成内容的质量。PICEPR在人格识别任务上达到5%-15%的性能提升，刷新当前最优水平。代码与模型权重可在https://research.jingjietan.com/?q=PICEPR获取。
原文摘要 · Abstract (English)
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.
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