SciIntBench: 衡量大语言模型在对抗性框架下对科研诚信规范的遵从度
SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing
AI总结：
提出SciIntBench对抗性基准，通过810个提示评估16个LLM在10个RCR类别中的框架敏感拒绝与帮助行为，发现模型对显性不当行为拒绝可靠，但对隐性违规（尤其是压力驱动的捷径）拒绝不足。
摘要：
大型语言模型（LLMs）越来越多地被用于支持科学工作，但尚不清楚它们是维护还是破坏负责任的研究行为（RCR）规范。我们引入了SciIntBench，这是一个对抗性基准，包含810个提示，涵盖10个RCR类别和三个科学领域。每个场景以显性对抗、隐性对抗和良性三种版本出现，使我们能够联合测量对不当行为的框架敏感拒绝以及对合法请求的帮助性。我们评估了来自六个提供商的16个商业和开源LLM（2024-2026年），产生了12,960个响应。我们发现，科研诚信对齐对框架高度敏感：模型拒绝显性不当行为远比拒绝隐性违规可靠得多，尤其是当不当行为被呈现为压力驱动的捷径时。拒绝率因RCR类别而异，在透明度、抄袭和捏造方面的边界较弱。
英文摘要：
Large language models (LLMs) are increasingly used to support scientific work, but it is unclear whether they uphold responsible conduct of research (RCR) norms or help undermine them. We introduce SciIntBench, an adversarial benchmark of 810 prompts across ten RCR categories and three scientific domains. Each scenario appears as an Overt Adversarial, Covert Adversarial, and Benign version, allowing us to jointly measure framing-sensitive refusal of misconduct and helpfulness on legitimate requests. We evaluate 16 commercial and open-weight LLMs from six providers (2024--2026), producing 12,960 responses. We find that scientific integrity alignment is strongly framing-sensitive: models refuse explicit misconduct far more reliably than covert violations, especially failing when misconduct is presented as a pressure-driven shortcut. Refusals vary by RCR category, with weaker boundaries around transparency, plagiarism, and fabrication.
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。
