What is Hallucination
Hallucination - AI generating plausible but incorrect or fabricated information
The phenomenon where AI models produce outputs that are fluent and plausible-sounding but factually incorrect, unsupported by the input, or entirely fabricated. Hallucination is a critical challenge in multimodal AI systems that affects trust and reliability.
How It Works
Hallucination occurs because generative models learn statistical patterns rather than factual knowledge. Language models predict probable next tokens based on training patterns, which can produce confident-sounding statements that are factually wrong. Multimodal models may describe objects not present in images, attribute incorrect actions to video scenes, or generate plausible but fabricated details. Hallucination is a fundamental property of current generative models, not a bug.
Technical Details
Types include intrinsic hallucination (contradicting the source input), extrinsic hallucination (adding information not in the source), factual hallucination (incorrect real-world facts), and faithfulness hallucination (not reflecting the retrieved context). Detection methods include natural language inference (NLI) models, fact-checking against knowledge bases, self-consistency checks, and specialized hallucination detectors. Mitigation strategies include RAG, grounding, constrained decoding, and RLHF.
Best Practices
- Use retrieval-augmented generation to ground outputs in verifiable source documents
- Implement hallucination detection in production pipelines before serving outputs to users
- Lower generation temperature and use constrained decoding for factual applications
- Design systems that acknowledge uncertainty rather than generating confident wrong answers
Common Pitfalls
- Assuming larger models hallucinate less when they may hallucinate more confidently
- Trusting model confidence scores as indicators of factual accuracy
- Not testing for hallucination on domain-specific content where models have less training data
- Using generative AI for high-stakes factual tasks without human verification
Advanced Tips
- Implement multi-step verification that cross-checks generated claims against multiple sources
- Use chain-of-thought prompting to make reasoning explicit and hallucination more detectable
- Build multimodal hallucination detectors that check generated text against source images or videos
- Apply calibrated confidence estimation to distinguish high-confidence facts from uncertain claims