HIPPD：用于人格检测的脑启发分层信息处理
HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection
AI总结：
本文提出脑启发的分层信息处理框架HIPPD，利用大语言模型、动态记忆模块和轻量级模型分别模拟大脑皮层、前额叶皮层和基底神经节，解决了多帖上下文捕捉和语义稀疏特征提取问题，在Kaggle和Pandora数据集上优于现有基线。
摘要：
文本人格检测旨在基于语言模式推断个体的人格特质。然而，现有的机器学习方法通常难以捕捉跨越多个帖子的上下文信息，且在语义稀疏环境中往往无法提取具有代表性和鲁棒性的特征。本文提出了HIPPD，这是一个模拟人脑分层信息处理的脑启发人格检测框架。HIPPD利用大语言模型模拟大脑皮层，实现全局语义推理和深度特征抽象。一个以前额叶皮层为原型的动态记忆模块，对关键特征执行自适应门控和选择性保留，所有调整均由多巴胺能预测误差反馈驱动。随后，一组模拟基底神经节的专用轻量级模型，通过严格的赢者通吃机制进行动态路由，以捕捉它们最擅长识别的人格相关模式。在Kaggle和Pandora数据集上的大量实验表明，HIPPD始终优于最先进的基线方法。
英文摘要：
Personality detection from text aims to infer an individual's personality traits based on linguistic patterns. However, existing machine learning approaches often struggle to capture contextual information spanning multiple posts and tend to fall short in extracting representative and robust features in semantically sparse environments. This paper presents HIPPD, a brain-inspired framework for personality detection that emulates the hierarchical information processing of the human brain. HIPPD utilises a large language model to simulate the cerebral cortex, enabling global semantic reasoning and deep feature abstraction. A dynamic memory module, modelled after the prefrontal cortex, performs adaptive gating and selective retention of critical features, with all adjustments driven by dopaminergic prediction error feedback. Subsequently, a set of specialised lightweight models, emulating the basal ganglia, are dynamically routed via a strict winner-takes-all mechanism to capture the personality-related patterns they are most proficient at recognising. Extensive experiments on the Kaggle and Pandora datasets demonstrate that HIPPD consistently outperforms state-of-the-art 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。
