AdversaRiskQA: 面向高风险领域的对抗事实性基准
AdversaRiskQA: An Adversarial Factuality Benchmark for High-Risk Domains
AI总结：
AdversaRiskQA是一个面向高风险领域的对抗事实性基准，通过评估LLM在不同领域和难度下的事实性表现，揭示模型弱点并提升高风险应用的可靠性。
摘要：
大型语言模型（LLMs）中的幻觉现象仍然是一个紧迫的问题，导致虚假信息的传播和公众信任的下降，尤其是在高风险领域。在幻觉类型中，事实性至关重要，因为它涉及模型与已确立的世界知识的一致性。对抗事实性被定义为故意在提示中插入错误信息，并以不同层次的表达自信程度进行插入，以测试模型检测和抵抗自信框架下虚假信息的能力。现有研究缺乏高质量、领域特定的资源来评估模型在这样的对抗条件下鲁棒性，也没有先前研究探讨了注入的虚假信息对长文本事实性的影响。为了解决这一差距，我们引入了AdversaRiskQA，这是首个经过验证和可靠的基准，系统评估了在健康、金融和法律领域中的对抗事实性。该基准包含两个难度级别，以测试LLMs在不同知识深度下的防御能力。我们提出了两种自动化方法来评估对抗攻击的成功率和长文本事实性。我们评估了来自Qwen、GPT-OSS和GPT家族的六个开源和闭源LLM，测量虚假信息检测率。长文本事实性在Qwen3（30B）上在基线和对抗条件下进行评估。结果表明，在排除无意义响应后，Qwen3（80B）实现了最高的平均准确率，而GPT-5保持了持续的高准确率。性能随着模型大小非线性增长，因领域而异，并且随着模型的增长，难度级别之间的差距缩小。长文本评估显示，注入的虚假信息与模型的事实输出之间没有显著相关性。AdversaRiskQA为识别LLM的弱点并开发更可靠的模型以应用于高风险领域提供了有价值的基准。
英文摘要：
Hallucination in large language models (LLMs) remains an acute concern, contributing to the spread of misinformation and diminished public trust, particularly in high-risk domains. Among hallucination types, factuality is crucial, as it concerns a model's alignment with established world knowledge. Adversarial factuality, defined as the deliberate insertion of misinformation into prompts with varying levels of expressed confidence, tests a model's ability to detect and resist confidently framed falsehoods. Existing work lacks high-quality, domain-specific resources for assessing model robustness under such adversarial conditions, and no prior research has examined the impact of injected misinformation on long-form text factuality. To address this gap, we introduce AdversaRiskQA, the first verified and reliable benchmark systematically evaluating adversarial factuality across Health, Finance, and Law. The benchmark includes two difficulty levels to test LLMs' defensive capabilities across varying knowledge depths. We propose two automated methods for evaluating the adversarial attack success and long-form factuality. We evaluate six open- and closed-source LLMs from the Qwen, GPT-OSS, and GPT families, measuring misinformation detection rates. Long-form factuality is assessed on Qwen3 (30B) under both baseline and adversarial conditions. Results show that after excluding meaningless responses, Qwen3 (80B) achieves the highest average accuracy, while GPT-5 maintains consistently high accuracy. Performance scales non-linearly with model size, varies by domains, and gaps between difficulty levels narrow as models grow. Long-form evaluation reveals no significant correlation between injected misinformation and the model's factual output. AdversaRiskQA provides a valuable benchmark for pinpointing LLM weaknesses and developing more reliable models for high-stakes applications.
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