BLUFF：跨58种低资源语言的虚假和合成内容检测基准测试
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages
AI总结：
BLUFF通过覆盖58种低资源语言的多语言基准测试，提供检测虚假和合成内容的全面框架，结合人工与AI生成内容，推动公平的虚假信息检测研究。
摘要：
多语言虚假信息威胁着全球信息完整性，但检测基准测试仍然局限于英语或少数高资源语言，使低资源语言社区缺乏稳健的防御工具。我们介绍了BLUFF，一个全面的虚假和合成内容检测基准测试，涵盖79种语言，超过202,000个样本，结合人工撰写并验证的内容（122,000多个样本，覆盖57种语言）和大语言模型（LLM）生成的内容（79,000多个样本，覆盖71种语言）。BLUFF独特地涵盖了高资源“大头”（20种）和低资源“长尾”（59种）语言，填补了多语言研究中检测虚假和合成内容的关键空白。我们的数据集包含四种内容类型（人工撰写、LLM生成、LLM翻译和混合人工-LLM文本），双向翻译（英语↔X），39种文本修改技术（36种伪造新闻操纵策略，3种AI编辑策略用于真实新闻），以及通过19种不同LLM生成的编辑强度变化。我们提出了AXL-CoI（对抗性跨语言代理交互链），一种新的多代理框架，用于受控的假/真新闻生成，配对mPURIFY，一个质量过滤管道，确保数据集完整性。实验表明，最先进的检测器在低资源语言与高资源语言之间，准确率下降高达25.3%。BLUFF为研究社区提供了多语言基准测试，广泛的语言导向基准评估，全面的文档和开源工具，以推进公平的虚假信息检测。数据集和代码可在：https://jsl5710.github.io/BLUFF/上获得。
英文摘要：
Multilingual falsehoods threaten information integrity worldwide, yet detection benchmarks remain confined to English or a few high-resource languages, leaving low-resource linguistic communities without robust defense tools. We introduce BLUFF, a comprehensive benchmark for detecting false and synthetic content, spanning 79 languages with over 202K samples, combining human-written fact-checked content (122K+ samples across 57 languages) and LLM-generated content (79K+ samples across 71 languages). BLUFF uniquely covers both high-resource "big-head" (20) and low-resource "long-tail" (59) languages, addressing critical gaps in multilingual research on detecting false and synthetic content. Our dataset features four content types (human-written, LLM-generated, LLM-translated, and hybrid human-LLM text), bidirectional translation (English$\leftrightarrow$X), 39 textual modification techniques (36 manipulation tactics for fake news, 3 AI-editing strategies for real news), and varying edit intensities generated using 19 diverse LLMs. We present AXL-CoI (Adversarial Cross-Lingual Agentic Chainof-Interactions), a novel multi-agentic framework for controlled fake/real news generation, paired with mPURIFY, a quality filtering pipeline ensuring dataset integrity. Experiments reveal state-of-theart detectors suffer up to 25.3% F1 degradation on low-resource versus high-resource languages. BLUFF provides the research community with a multilingual benchmark, extensive linguistic-oriented benchmark evaluation, comprehensive documentation, and opensource tools to advance equitable falsehood detection. Dataset and code are available at: https://jsl5710.github.io/BLUFF/
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。
