标题： 识别、预测和缓解大语言模型对在线行为研究的污染 显示英文标题

标题： Recognising, Anticipating, and Mitigating LLM Pollution of Online Behavioural Research

摘要： 在线行为研究面临一种新兴威胁，因为参与者越来越多地转向大型语言模型（LLMs）寻求建议、翻译或任务委托：即LLM污染。 我们识别出三种相互作用的变体，这些变体威胁着在线行为研究的有效性和完整性。 首先，部分LLM中介是指参与者有选择性地使用LLM来完成任务的某些方面，例如翻译或措辞支持，这导致研究人员（错误地）将LLM生成的输出视为人类的输出。 其次，完全LLM委托是指具有自主性的LLM在几乎没有人类监督的情况下完成研究，从更基础的层面削弱了以人类被试为中心的研究核心前提。 第三，LLM溢出效应指的是人类参与者开始预期在线研究中存在LLM的存在，从而改变自己的行为，即使实际上并未涉及LLM。 虽然部分中介和完全委托构成了自动化程度不断上升的连续体，但LLM溢出效应反映了二阶反应效应。 这三种变体相互作用，并产生级联的扭曲，损害样本的真实性，引入难以在事后检测的偏差，并最终削弱基于人类认知和行为的在线研究的认识论基础。 至关重要的是，LLM污染的威胁已经随着生成式AI的进步而共同进化，形成了不断升级的方法论军备竞赛。 为应对这一问题，我们提出了一种多层应对措施，涵盖研究者实践、平台责任和社区努力。 随着挑战的演变，协调适应将是确保方法论完整性和保护在线行为研究有效性的关键。 显示英文摘要

摘要： Online behavioural research faces an emerging threat as participants increasingly turn to large language models (LLMs) for advice, translation, or task delegation: LLM Pollution. We identify three interacting variants through which LLM Pollution threatens the validity and integrity of online behavioural research. First, Partial LLM Mediation occurs when participants make selective use of LLMs for specific aspects of a task, such as translation or wording support, leading researchers to (mis)interpret LLM-shaped outputs as human ones. Second, Full LLM Delegation arises when agentic LLMs complete studies with little to no human oversight, undermining the central premise of human-subject research at a more foundational level. Third, LLM Spillover signifies human participants altering their behaviour as they begin to anticipate LLM presence in online studies, even when none are involved. While Partial Mediation and Full Delegation form a continuum of increasing automation, LLM Spillover reflects second-order reactivity effects. Together, these variants interact and generate cascading distortions that compromise sample authenticity, introduce biases that are difficult to detect post hoc, and ultimately undermine the epistemic grounding of online research on human cognition and behaviour. Crucially, the threat of LLM Pollution is already co-evolving with advances in generative AI, creating an escalating methodological arms race. To address this, we propose a multi-layered response spanning researcher practices, platform accountability, and community efforts. As the challenge evolves, coordinated adaptation will be essential to safeguard methodological integrity and preserve the validity of online behavioural research.