Large language models eroding science understanding: an empirical study of malignment
This paper is accepted and in press for AI and Ethics. This paper includes the supplementary data file at the end of the manuscript. This study examines whether large language models (LLMs) can reliably answer scientific questions and demonstrates how easily they can be influenced by fringe scientific material. The authors modified custom LLMs to prioritise knowledge in selected fringe papers on the Fine Structure Constant and Gravitational Waves, then compared their responses with those of domain experts and standard LLMs. The altered models produced fluent, convincing answers that contradicted scientific consensus and were difficult for non-experts to detect as misleading. The results s...
| Authors | Harry Collins, Hartmut Grote, Paul Newbury et al. |
|---|---|
| Primary category | cs.CY |
| Published | 28 April 2026 |
| Last updated at source | 17 September 2026 |
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