Epistemic Fragility in Large Language Models: Prompt Framing Systematically Modulates Misinformation Correction

TL;DR

This study reveals that prompt framing significantly influences the ability of large language models to correct misinformation, with certain framings reducing correction effectiveness and highlighting structural vulnerabilities in LLMs.

Contribution

It systematically demonstrates how prompt framing affects misinformation correction in LLMs and uncovers model-specific differences in correction capabilities.

Findings

Creative prompts reduce correction likelihood

Expert role prompts improve correction effectiveness

Model Gemini 2.5 Pro performs worse than Claude Sonnet 4.5

Abstract

As large language models (LLMs) rapidly displace traditional expertise, their capacity to correct misinformation has become a core concern. We investigate the idea that prompt framing systematically modulates misinformation correction - something we term 'epistemic fragility'. We manipulated prompts by open-mindedness, user intent, user role, and complexity. Across ten misinformation domains, we generated 320 prompts and elicited 2,560 responses from four frontier LLMs, which were coded for strength of misinformation correction and rectification strategy use. Analyses showed that creative intent, expert role, and closed framing led to a significant reduction in correction likelihood and effectiveness of used strategy. We also found striking model differences: Gemini 2.5 Pro had 74% lower odds of strong correction than Claude Sonnet 4.5. These findings highlight epistemic fragility as an important structural property of LLMs, challenging current guardrails and underscoring the need for alignment strategies that prioritize epistemic integrity over conversational compliance.