BIG5-TPoT：通过文本的目标预选择预测大五人格特质、子维度及条目
BIG5-TPoT: Predicting BIG Five Personality Traits, Facets, and Items Through Targeted Preselection of Texts
AI总结：
针对大文本量下人格预测的挑战，提出文本目标预选择（TPoT）策略构建BIG5-TPoT模型，通过语义过滤相关文本解决大模型输入限制，同时提升意识流散文数据集的预测指标。
摘要：
从个体生成的文本中预测其人格是一项具有挑战性的任务，尤其是当文本量较大时。本文提出一种简单却有效的新策略——文本目标预选择（TPoT）。该方法对输入深度学习模型的文本进行语义过滤，专门用于预测大五人格特质、子维度或条目，即BIG5-TPoT模型。通过选择与特定特质、子维度或条目语义相关的文本，该策略不仅解决了大型语言模型的输入文本限制问题，还提升了意识流散文数据集预测任务中的平均绝对误差和准确率指标。
英文摘要：
Predicting an individual's personalities from their generated texts is a challenging task, especially when the text volume is large. In this paper, we introduce a straightforward yet effective novel strategy called targeted preselection of texts (TPoT). This method semantically filters the texts as input to a deep learning model, specifically designed to predict a Big Five personality trait, facet, or item, referred to as the BIG5-TPoT model. By selecting texts that are semantically relevant to a particular trait, facet, or item, this strategy not only addresses the issue of input text limits in large language models but also improves the Mean Absolute Error and accuracy metrics in predictions for the Stream of Consciousness Essays dataset.
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。
