Recognising and mitigating LLM Pollution in online behavioural research.
Where this comes from
- Record sourced from PubMed, PMID 42350412.
- Also identified by DOI 10.1038/s41467-026-74621-9 and PMC identifier 13303828.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Online behavioural research faces a growing methodological and epistemic threat as participants increasingly rely on large language models: <i>LLM Pollution</i>. Amid accumulating empirical evidence of contamination, we introduce a conceptual framework that distinguishes three variants — <i>Partial LLM Mediation</i>, <i>Full LLM Delegation</i>, and <i>LLM Spillover</i>. Their interaction distorts samples, biases inferences, and fuels an escalating methodological arms race. We outline mitigation strategies spanning researcher practices, platform accountability, and community adaptation.