对比编码器与解码器型Transformer在社交机器人检测中的表现
A Comparative Analysis of Transformer Models in Social Bot Detection
- 用统一流程评估编码器与解码器型Transformer的检测能力
- 编码器模型准确率更高，解码器模型更适应不同任务
- 适合关注模型泛化能力与实际部署的AI安全研究者
社交媒体已成为当今社会的关键沟通渠道。这一现实促使多方使用人工用户（即机器人）误导他人相信虚假信息或为其自身利益行动。大型语言模型等先进文本生成工具进一步加剧了这一问题。本文旨在比较基于编码器与解码器的Transformer模型在社交机器人检测中的有效性。构建了标准化评估流程，结果表明编码器型分类器展现出更高的准确率与鲁棒性，而解码器型模型则通过任务特异性对齐表现出更强的适应性，显示出在多种应用场景中更好的泛化潜力。这些发现有助于遏制数字环境被操纵，维护在线讨论的完整性。
原文摘要 · Abstract (English)
Social media has become a key medium of communication in today's society. This realisation has led to many parties employing artificial users (or bots) to mislead others into believing untruths or acting in a beneficial manner to such parties. Sophisticated text generation tools, such as large language models, have further exacerbated this issue. This paper aims to compare the effectiveness of bot detection models based on encoder and decoder transformers. Pipelines are developed to evaluate the performance of these classifiers, revealing that encoder-based classifiers demonstrate greater accuracy and robustness. However, decoder-based models showed greater adaptability through task-specific alignment, suggesting more potential for generalisation across different use cases in addition to superior observa. These findings contribute to the ongoing effort to prevent digital environments being manipulated while protecting the integrity of online discussion.
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