Overview
Hallucination mitigation encompasses the technical and design strategies used to reduce the frequency and impact of language models generating confident but factually incorrect outputs. Hallucinations arise from the model’s training objective — predicting plausible next tokens — which does not distinguish between correct and incorrect claims. Mitigation is multi-layered: grounding connects outputs to verifiable sources, sampling strategies reduce confidence-without-basis, self-consistency checks surface contradictions, and evaluation frameworks measure residual error rates. No single technique eliminates hallucinations; production systems combine several in defense-in-depth.
Key Concepts
- Grounding and retrieval — Providing explicit source documents at inference time constrains the model to evidence it can cite, reducing reliance on parametric memory.
- Self-consistency sampling — Generating multiple responses at non-zero temperature and comparing outputs. Claims that appear in most samples are more reliable; divergence signals uncertainty.
- Chain-of-thought with verification — Requiring the model to show reasoning steps makes errors more visible and enables intermediate verification before final output is committed.
- Confidence calibration — Training models (or prompting them) to express uncertainty proportional to actual reliability. A calibrated model says “I’m not sure” when it should.
- Output validation pipelines — Post-generation systems that check outputs against known-good data, structured schemas, or independent model judgments before returning them to the user.
- Constitutional AI and critique — A second model pass (or a critic agent in a multi-agent system) reviews the primary output for factual consistency and flags suspect claims.
Trade-offs
- More verification passes improve accuracy but multiply latency and cost
- Retrieval-based grounding only mitigates hallucinations about retrievable facts — reasoning errors remain
- Overly aggressive uncertainty expression makes the system less useful even when it would have been correct
- Self-consistency requires temperature > 0, which introduces non-determinism into otherwise reproducible pipelines
Related
- AI & Agents — the system context in which hallucination risk is highest (autonomous action on incorrect beliefs)
- Grounding — the primary technical strategy for constraining model outputs to verifiable information
- Evaluation Frameworks — how hallucination rates are measured and tracked over time
- Multi-Agent Systems — critic and verifier agents are a structural mitigation approach