AI hallucination is the tendency of generative models—especially large language models and multimodal systems—to produce fluent, confident outputs that are factually incorrect, logically inconsistent, fabricated, or unsupported by evidence. Hallucinations arise from next-token prediction over imperfect training distributions, weak grounding, or mis-specified decoding constraints, and can manifest as false citations, invented data, or non-existent APIs.
What is AI Hallucination?
Hallucination is a mismatch between linguistic plausibility and truth. Autoregressive models optimize likelihood, not factuality, so they may interpolate or extrapolate beyond provided context. Typical drivers include domain or temporal shift (out-of-date pretraining), retrieval failures, overly aggressive sampling (high temperature, nucleus), and prompts that conflate instructions with untrusted context. Lack of verifiers, weak tool use, and exposure bias worsen drift. Mitigations re-anchor generation to sources and constraints: retrieval-augmented generation (RAG) with citations, constrained/grammar decoding, function calling for deterministic computations, evaluator/guardian models for fact checking, and conservative decoding policies tuned to the application’s risk tolerance.
Why it matters and where it’s used
Hallucinations erode trust and can cause harm in regulated or high-stakes settings. In customer support, they produce incorrect guidance; in coding, phantom APIs; in healthcare/finance/legal, risky advice; in analytics, made-up numbers. Enterprises counter this with RAG, tool execution for calculations/queries, provenance tracking, and human-in-the-loop review for sensitive actions. Clear UX (confidence, citations) manages expectations.
Examples
- Fictitious citation: cites a non-existent paper with plausible authors and venue.
- Fabricated field: adds a field in an API response that compiles but fails at runtime.
- Misattributed quote: appears in a news summary due to entity confusion.
- Confident but wrong numeric answer when a calculator tool was not invoked.
FAQs
- Does bigger mean fewer hallucinations? Larger models help but do not eliminate them without grounding and verification.
- How do I reduce them? Use RAG with strict citation prompts, function calling for computations/queries, constrained decoding, and evaluator models; tune temperature and top-p.
- Can I detect hallucinations automatically? Partially—use factuality checkers, retrieval consistency checks, and self-critique prompts, but retain reviews for high risk.
- Are citations enough? Require grounded quotes/snippets with source IDs and enforce refusal when evidence is insufficient.
- What metrics should I track? Faithfulness, answer correctness, citation precision/recall, and downstream task success; audit red-team cases.
- Does fine-tuning help? Instruction tuning plus preference optimization (DPO/RLHF) reduces risky styles, but grounding/tools are still needed.