LLM Hallucination Mitigation is the collection of techniques — architectural, training-time, and inference-time — designed to reduce the rate at which Large Language Models generate text that is fluent and confident but factually incorrect, unsupported by the provided context, or internally contradictory.

Why LLMs Hallucinate

• Training Objective: Language models are trained to predict the most likely next token, not the most truthful one. Fluency and factual accuracy are correlated but not identical.
• Knowledge Cutoff: Parametric knowledge is frozen at pretraining time. Questions about events, products, or data after that cutoff receive smoothly fabricated answers.
• Long-Tail Facts: Rare facts appear infrequently in training data. The model assigns low confidence internally but generates confidently because the decoding strategy selects the highest-probability continuation regardless of calibration.

Mitigation Strategy Stack

• Retrieval-Augmented Generation (RAG): Ground the model by injecting relevant retrieved documents into the prompt. The LLM is instructed to answer only from the provided context. RAG reduces hallucination on knowledge-intensive tasks by 30-60% compared to closed-book generation, though the model can still ignore or misinterpret retrieved passages.
• Fine-Tuning for Faithfulness: RLHF (Reinforcement Learning from Human Feedback) with reward models trained to penalize unsupported claims teaches the model to hedge ("I don't have information about...") rather than fabricate. Constitutional AI and DPO (Direct Preference Optimization) achieve similar alignment with less reward model engineering.
• Chain-of-Thought with Verification: Force the model to show its reasoning steps, then run a separate verifier (another LLM or a symbolic checker) that validates each claim against the source documents. Claims that cannot be traced to evidence are flagged or suppressed.
• Constrained Decoding: At generation time, restrict the output vocabulary or structure to avoid free-form generation where hallucination is highest. Structured output (JSON with predefined fields) and tool-call grounding (forcing the model to call a search API before answering) reduce the hallucination surface.

Measuring Hallucination

Automated metrics include FActScore (decomposing responses into atomic claims and checking each against Wikipedia), ROUGE-L against gold references, and NLI-based faithfulness scores that classify each generated sentence as entailed, neutral, or contradicted by the source.

LLM Hallucination Mitigation is the critical reliability engineering layer that separates a research demo from a production AI system — without systematic grounding and verification, every fluent LLM response carries an unknown probability of being confidently wrong.