Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts
Detecting machine-generated disinformation on social media is increasingly difficult as large language models (LLMs) make it easier to generate and rewrite misleading content at scale. Static benchmark evaluations, measuring detector performance on fixed held-out datasets, do not capture how detectors behave when posts are deliberately transformed to evade classification. This paper adapts the Build it, Break it, Fix it framework into Build it, Break it, Repeat (BiBiR): iterative sessions designed to stress-test detectors' robustness under iterative adversarial conditions, evaluating whether models remain reliable when disinformation posts are systematically transformed to evade classific...
| Authors | Kevin Thomas, Milosz Kasprzyk, Reuel C Igbokwe Onuigbo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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