从帖子到人格：利用大语言模型开展社交媒体MBTI预测
From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media
AI总结：
针对大语言模型幻觉及MBTI类别分布不均两大挑战，提出融合检索增强生成与合成少数类过采样的PtoP框架，在社交媒体MBTI预测任务上超越10种基线，达到当前最优性能。
摘要：
基于社交媒体帖子的人格预测是一项关键任务，在心理学与社会学领域有诸多应用。迈尔斯-布里格斯类型指标（MBTI）是一种常用的人格测评工具，传统上通过机器学习（ML）和深度学习（DL）技术进行预测。近年来，大语言模型（LLMs）的成功展现出其从社交媒体内容中理解、推断人格特质的巨大潜力。然而，直接利用大语言模型开展MBTI预测面临两大核心挑战：一是大语言模型固有的幻觉问题，二是人群中MBTI类型天然存在的分布不均衡。本文提出PostToPersonality（PtoP），这是一种新型的基于大语言模型的框架，用于根据个人社交媒体帖子预测MBTI类型。具体而言，PtoP采用结合上下文学习的检索增强生成技术，以缓解大语言模型的幻觉问题。此外，我们对预训练大语言模型进行微调，并结合合成少数类过采样技术——该技术通过生成合成样本平衡类别不均衡——以提升模型对MBTI理解的针对性。在真实世界社交媒体数据集上开展的实验表明，与10种机器学习和深度学习基线模型相比，PtoP取得了当前最优的性能。
英文摘要：
Personality prediction from social media posts is a critical task that implies diverse applications in psychology and sociology. The Myers Briggs Type Indicator (MBTI), a popular personality inventory, has been traditionally predicted by machine learning (ML) and deep learning (DL) techniques. Recently, the success of Large Language Models (LLMs) has revealed their huge potential in understanding and inferring personality traits from social media content. However, directly exploiting LLMs for MBTI prediction faces two key challenges: the hallucination problem inherent in LLMs and the naturally imbalanced distribution of MBTI types in the population. In this paper, we propose PostToPersonality (PtoP), a novel LLM based framework for MBTI prediction from social media posts of individuals. Specifically, PtoP leverages Retrieval Augmented Generation with in context learning to mitigate hallucination in LLMs. Furthermore, we fine tune a pretrained LLM to improve model specification in MBTI understanding with synthetic minority oversampling, which balances the class imbalance by generating synthetic samples. Experiments conducted on a real world social media dataset demonstrate that PtoP achieves state of the art performance compared with 10 ML and DL baselines.
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。
