通过嵌入融合增强作者归属识别：一种结合掩码语言模型与编码器-解码器语言模型的新方法
Enhancing Authorship Attribution through Embedding Fusion: A Novel Approach with Masked and Encoder-Decoder Language Models
AI总结：
针对AI生成与人类撰写文本的区分需求，提出基于多预训练语言模型嵌入融合的作者归属识别框架，在五类大模型生成的平衡数据集上实现超96%的分类准确率与0.93以上的MCC，验证了方法的有效性与鲁棒性。
摘要：
AI生成内容与人类撰写文本的日益普及，凸显了可靠区分方法的必要性。为应对这一挑战，我们提出一个利用预训练语言模型（PLMs）文本嵌入的新框架，用于区分AI生成文本与人类创作文本。我们的方法采用嵌入融合（Embedding Fusion）整合多个语言模型的语义信息，利用它们的互补优势提升性能。通过在公开的多样化数据集上开展广泛评估，我们提出的方法表现出优异性能，分类准确率超过96%，马修斯相关系数（MCC）超过0.93。该评估在由五种知名大语言模型（LLMs）生成文本构成的平衡数据集上进行，凸显了我们新方法的有效性与鲁棒性。
英文摘要：
The increasing prevalence of AI-generated content alongside human-written text underscores the need for reliable discrimination methods. To address this challenge, we propose a novel framework with textual embeddings from Pre-trained Language Models (PLMs) to distinguish AI-generated and human-authored text. Our approach utilizes Embedding Fusion to integrate semantic information from multiple Language Models, harnessing their complementary strengths to enhance performance. Through extensive evaluation across publicly available diverse datasets, our proposed approach demonstrates strong performance, achieving classification accuracy greater than 96% and a Matthews Correlation Coefficient (MCC) greater than 0.93. This evaluation is conducted on a balanced dataset of texts generated from five well-known Large Language Models (LLMs), highlighting the effectiveness and robustness of our novel methodology.
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。
