- Cross-verify With Trusted Sources
Always corroborate key facts or figures generated by the LLM with reliable, authoritative sources such as official websites, academic papers, or verified databases. If the output contradicts these trusted references, it’s likely a hallucination. - Check Logical Consistency
Review the output for internal contradictions or implausible claims. Hallucinated content often contains inconsistencies that don’t align with common sense or known domain knowledge. - Context Alignment
Verify if the model’s response aligns with the provided prompt or context. Sometimes LLMs generate answers that seem relevant but actually introduce unsupported information not grounded in the prompt or known facts. - Look for Fabricated Details
Be cautious of overly specific information, such as invented names, dates, statistics, or citations that cannot be found or verified elsewhere. These fabricated details are common signs of hallucinations. - Evaluate Model Confidence Metrics
Use available model confidence or likelihood scores (e.g., log probability of generated sequences). Low confidence scores often indicate potential hallucinations, signaling that the model is less certain about the correctness of the output.
By applying these practical checks, data scientists and users can better detect and mitigate hallucinations, improving the reliability and safety of AI-generated content.