人工智能是否正在赶上人类表达？探索英语和阿拉伯语中情感、个性、作者身份和语言风格的模仿
Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language Models
AI总结：
本文研究六种大语言模型在英语和阿拉伯语中模仿情感和个性的能力，发现AI生成文本可被识别，但分类性能下降，揭示了LLM在情感信号编码上的差异。
摘要：
随着LLM流畅度的提升，其模拟复杂人类特质如情感表达和个性的能力受到质疑。本研究探讨LLM是否能有效模仿英语中的情感细微差别和阿拉伯语中的个性标记。通过六个模型（Jais、Mistral、LLaMA、GPT-4o、Gemini和DeepSeek）进行两项任务：区分人类和AI生成文本，以及评估LLM生成文本的情感或个性特征与人类的相似性。结果表明，AI生成文本可被识别（F1>0.95），但在改写样本上分类性能下降，表明依赖表面风格线索。情感和个性分类实验显示显著的泛化差距：人类数据训练的分类器在AI生成文本上表现差，反之亦然，表明LLM编码情感信号的方式不同于人类。重要的是，增加AI生成数据训练可提升阿拉伯语个性分类任务的表现，突显合成数据在资源匮乏语言中的潜力。模型特定分析显示GPT-4o和Gemini在情感一致性上表现最佳。语言和心理语言学分析揭示了人类和AI文本在语气、真实性和文本复杂性上的可测量差异。这些发现对情感计算、作者身份归属和负责任的AI部署具有影响，特别是在资源匮乏语言背景下，生成AI检测和对齐面临独特挑战。
英文摘要：
The advancing fluency of LLMs raises important questions about their ability to emulate complex human traits, including emotional expression and personality, across diverse linguistic and cultural contexts. This study investigates whether LLMs can convincingly mimic emotional nuance in English and personality markers in Arabic, a critical under-resourced language with unique linguistic and cultural characteristics. We conduct two tasks across six models:Jais, Mistral, LLaMA, GPT-4o, Gemini, and DeepSeek. First, we evaluate whether machine classifiers can reliably distinguish between human-authored and AI-generated texts. Second, we assess the extent to which LLM-generated texts exhibit emotional or personality traits comparable to those of humans. Our results demonstrate that AI-generated texts are distinguishable from human-authored ones (F1>0.95), though classification performance deteriorates on paraphrased samples, indicating a reliance on superficial stylistic cues. Emotion and personality classification experiments reveal significant generalization gaps: classifiers trained on human data perform poorly on AI-generated texts and vice versa, suggesting LLMs encode affective signals differently from humans. Importantly, augmenting training with AI-generated data enhances performance in the Arabic personality classification task, highlighting the potential of synthetic data to address challenges in under-resourced languages. Model-specific analyses show that GPT-4o and Gemini exhibit superior affective coherence. Linguistic and psycholinguistic analyses reveal measurable divergences in tone, authenticity, and textual complexity between human and AI texts. These findings have implications for affective computing, authorship attribution, and responsible AI deployment, particularly within underresourced language contexts where generative AI detection and alignment pose unique challenges.
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。
