AI生成的说服能否被检测？Persuaficial基准与AI与人类语言差异
Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences
AI总结：
本文探讨LLM生成的说服内容是否更难自动检测，通过Persuaficial基准对比人类与AI生成文本的语言差异，揭示细微说服策略对检测性能的影响。
摘要：
大型语言模型（LLMs）能够生成具有高度说服力的文本，引发了对其在宣传、操控等有害用途中被滥用的担忧。这促使我们提出核心问题：LLM生成的说服是否比人类撰写的说服更难自动检测？为此，我们分类可控生成方法以产生具有说服力的内容，并引入Persuaficial，一个覆盖六种语言（英语、德语、波兰语、意大利语、法语和俄语）的高质量多语言基准。利用此基准，我们进行了广泛的实证评估，比较人类撰写的和LLM生成的说服性文本。我们发现，尽管显而易见的LLM生成文本可能比人类撰写的文本更容易被检测到，但细微的LLM生成说服策略始终会降低自动检测性能。除了检测性能外，我们还提供了首次全面的语言分析，对比人类和LLM生成的说服文本，为开发更具解释性和鲁棒性的检测工具提供见解。
英文摘要：
Large Language Models (LLMs) can generate highly persuasive text, raising concerns about their misuse for propaganda, manipulation, and other harmful purposes. This leads us to our central question: Is LLM-generated persuasion more difficult to automatically detect than human-written persuasion? To address this, we categorize controllable generation approaches for producing persuasive content with LLMs and introduce Persuaficial, a high-quality multilingual benchmark covering six languages: English, German, Polish, Italian, French and Russian. Using this benchmark, we conduct extensive empirical evaluations comparing human-authored and LLM-generated persuasive texts. We find that although overtly persuasive LLM-generated texts can be easier to detect than human-written ones, subtle LLM-generated persuasion consistently degrades automatic detection performance. Beyond detection performance, we provide the first comprehensive linguistic analysis contrasting human and LLM-generated persuasive texts, offering insights that may guide the development of more interpretable and robust detection tools.
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。
