LLMs believe false statements even after explicit warnings that they're false

New research reveals that large language models (LLMs) can absorb false statements even when explicitly labeled as false, indicating a tendency for 'negation neglect' that complicates the reliability of AI-generated information. This finding suggests that the way training data is structured could significantly impact the accuracy of LLM outputs.

Source: Ars Technica AI