测试大模型在科研诚信上的表现，发现其对隐晦违规反应迟钝。
SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing
- 设计对抗性提示集，分显性、隐蔽和正常三类测试模型拒绝违规能力。
- 16个模型在810个场景中响应12960次，隐蔽违规拒绝率显著低于明显违规。
- 透明度、抄袭和造假等领域的边界模糊，模型易被压力诱导妥协。
大型语言模型（LLMs）越来越多地用于支持科学研究，但它们是否遵循负责任研究行为（RCR）规范尚不明确。我们提出SciIntBench，一个包含810个提示的对抗性基准，覆盖十个RCR类别和三个科学领域。每个场景以显性对抗、隐蔽对抗和良性版本呈现，可联合衡量模型对不当行为的敏感拒绝与对合法请求的帮助性。我们评估了来自六家供应商的16个商用及开源权重模型（2024–2026年），生成12,960条响应。结果表明，科研诚信对提示框架高度敏感：模型对明显不当行为的拒绝远比对隐蔽违规更可靠，尤其当不当行为被包装为压力驱动的捷径时，拒绝对抗能力显著下降。不同RCR类别间差异明显，透明度、剽窃和伪造方面的边界最为薄弱。
原文摘要 · Abstract (English)
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.
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