LLM “hallucinations” come from predictable mechanics: missing or noisy training data, the model’s probabilistic decoding choices, and distributional mismatch between training and deployment. Fixes follow directly: better grounding (RAG), smarter decoding/verification, and rigorous handling of distribution shift in production.
Open with the bottom line
Models don’t “lie” — they predict. When the prediction process encounters missing facts, ambiguous prompts, or a different input distribution than it saw during training, it fills gaps with the most statistically likely continuation. That fluent “filling-in” looks convincing — which is why hallucinations are dangerous in products.
1) Data gaps & noisy training signals — the raw-material problem
Think of a model as a statistics engine trained on a giant, messy library. If the library lacks authoritative coverage for a topic (or contains contradictory/noisy content), the model will generalize from weak patterns and invent plausible-sounding facts. Two common sub-causes:
- Coverage gaps: rare entities, newly emerged facts, or niche domains simply aren’t in the training set.
- Noisy/conflicting sources: web text often repeats inaccuracies; models learn those pattern associations and may amplify them.
When domain data is missing or low-quality, the model has nothing reliable to “copy” — so it synthesizes based on nearest patterns. This is a primary, empirically observed source of hallucination.
Mitigation (data): curate domain-specific corpora, maintain updatable knowledge stores, and use retrieval to pull authoritative snippets at inference time. RAG-style grounding has shown large practical reductions in hallucination when implemented carefully.
2) Decoding & sampling — creativity by design
The model’s internal objective is to predict the next token with high likelihood — not to verify facts. At inference time, decoding algorithms (temperature, top-k, nucleus sampling) control how creative the output is. Higher randomness → more creative but also higher risk of invention. Even deterministic decoding (beam/greedy) can produce confident-sounding errors when the model’s internal beliefs are inaccurate.
Practical implication: choosing decoding parameters is a trade-off: creative copy vs factual reliability. For fact-heavy tasks, prefer deterministic decoding and stricter stopping criteria. Advanced decoding strategies (contrastive or summary-guided decoding) can reduce hallucinations in some settings.
3) Distributional shift — train vs real world
Models are trained on past data samples. When the production input distribution (user prompts, domain style, new terminology) differs from that training distribution, the model’s learned associations can break down. Small perturbations can magnify into big errors — especially in multilingual, translation, or low-resource settings.
Mitigation (deployment): monitor input drift, log failure cases, and retrain or fine-tune on real production queries. Use prompt engineering to constrain inputs into model-friendly formats and add fallback flows when confidence is low.
4) Model priors & the “plausibility” trap
Because models learn statistical correlations, they develop priors — default beliefs about how language usually continues. Those priors favor fluent, commonsense-sounding completions, even if they’re incorrect for specific facts. When evidence is weak or absent, priors win. Recent theoretical work frames hallucination as a dominance of hallucinatory subsequence associations over faithful ones — a helpful way to reason about why models invent rather than abstain.
Mitigation (model-level): tune fine-tuning objectives to penalize inventing (contrastive or faithfulness-focused losses), or use smaller specialist verifiers to re-score candidate outputs.
5) Multimodal & interface pitfalls
In vision-language and multimodal models the same mechanics apply: weak or absent visual evidence, poor OCR, or bad alignment between modalities leads the model to “fill-in” visual claims. The UI also matters: if your product hides source images or context, operators are more likely to accept hallucinated claims as truth.
Mitigation (UX + model): surface image snippets, run modality-specific detectors (OCR, object detectors), and show provenance in the interface.
Detection signals you can implement today
- Retrieval coverage check: flag claims with no supporting retrieved snippets.
- Agreement across runs: run multiple seeds/models — low agreement → higher hallucination risk.
- Schema/rule checks: validate dates, numeric ranges, and entity formats programmatically.
- Lightweight faithfulness classifier: train/check a compact model that scores whether output aligns with sources.
Practical product checklist (before shipping)
- Is the claim backed by a retrieved source snippet? If no → block or label.
- Was decoding set for reliability (lower temperature / deterministic)? If not → re-run.
- Is the input distribution close to training? If not → route to fallback or human review.
- Are multimodal claims backed by detectors (OCR/object detection)? If not → show image & flag.
Quick real-world example
A legal assistant generates a statute citation for a novel legal argument. If the training data lacks that niche statute (data gap) and the prompt nudges the model toward completion (decoding) while users expect instant answers (distributional pressure), the assistant invents a convincing but false citation. The solution: RAG to an authoritative legal corpus, deterministic decoding, and mandatory human sign-off for citations.
Final thoughts — treat hallucination as an engineering problem
Hallucinations are not mystical: they’re the result of concrete, diagnosable interactions between data, training objectives, decoding choices, and deployment conditions. That means you can prioritize interventions that give the best ROI: start with retrieval + provenance, tune decoding for reliability, monitor distributional drift, and add verification layers where risk matters most. Recent research and production experience confirm this roadmap.
