What Does a Sharing Question Add? Auditing LLM Survey Scores for Misinformation
Evaluating misinformation requires distinguishing whether readers believe content from whether they would share it. Asking large language models (LLMs) both questions yields two scores, but does the sharing answer contribute information beyond the credibility answer? We audit eight model versions on 290 synthetic misinformation articles, using 1,256 paired survey responses with 317 participant identifiers as an external validity criterion. An initial reversal motivates the audit: every model's raw sharing score predicts mean human sharing less accurately than its credibility score. This ordering changes after offset correction, so it does not by itself diagnose missing information. We ins...
| Authors | Zonghuan Xu, Xiang Zheng, Yutao Wu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 April 2026 |
| Last updated at source | 1 October 2026 |
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