六款大模型在英阿语中模仿情感与个性，发现仍可被识别且差异明显。
Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language Models
- 用机器分类器对比人写与AI生成文本，发现能准确区分（F1>0.95）
- AI文本在情绪和人格特征上与人类有显著差异，跨数据集泛化差
- 增补合成数据提升阿拉伯语人格识别效果，适合低资源语言研究
随着大语言模型（LLMs）的日益流畅，其在跨语言与文化背景下模拟复杂人类特质（如情感表达与人格特征）的能力备受关注。本研究评估六款模型（Jais、Mistral、LLaMA、GPT-4o、Gemini、DeepSeek）在英语情感与阿拉伯语人格方面的表现。首先，机器分类器可可靠区分人类与AI生成文本（F1>0.95），但对改写样本识别能力下降，表明依赖表面风格线索。其次，基于人类数据训练的分类器在AI文本上表现不佳，反之亦然，说明模型编码情感信号方式与人类不同。值得注意的是，使用合成数据微调后，阿拉伯语人格分类性能提升。模型分析显示，GPT-4o与Gemini在情感连贯性上更优。语言学与心理语言学分析揭示人类与AI文本在语气、真实感和文本复杂度上存在可观测差异。研究对情感计算、作者溯源与负责任的AI部署具有重要意义，尤其在低资源语言场景下，生成式AI检测与对齐面临独特挑战。
原文摘要 · Abstract (English)
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.
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