Hallucination Mitigation: RAG, Decoding, and Training › Summary

Hallucination mitigation combines strategies across the generation pipeline, from knowledge sourcing through to output communication:

• Retrieval-augmented generation addresses the knowledge gap by supplying relevant documents at inference time. It is the most impactful single mitigation technique, but requires a high-quality knowledge base and attention to context faithfulness. Its failure modes include retrieval failure, context ignoring, and unfaithful extension beyond retrieved content.
• Decoding strategies modify generation behavior without changing model weights. Temperature tuning, top-p filtering, and self-consistency decoding improve factual accuracy by reducing variance and requiring cross-sample agreement. Contrastive decoding amplifies what the large model has uniquely learned relative to a smaller model.
• Training approaches include SFT on factual data, RLHF with factuality-aware human preferences, DPO on hallucination/truthful response pairs, knowledge-grounded fine-tuning, and surgical fact editing. These modify the model's intrinsic behavior but require quality training data and do not eliminate the problem.
• Prompt engineering provides low-cost gains by asking for uncertainty expression, requiring citations, and decomposing complex questions into sub-questions before answering.
• Post-hoc verification uses NLI models, LLM-based fact checking, or structured frameworks like FActScore to verify generated claims against reference knowledge after generation.
• Uncertainty expression treats communication of uncertainty as a feature. Verbalized hedges, calibrated abstention, token-level entropy signals, and selective prediction help users understand when to trust model output.

Effective hallucination mitigation in production typically combines all of these categories. RAG reduces factual errors for covered queries; decoding strategies add consistency guarantees; training reduces systematic biases; uncertainty expression handles the remainder by surfacing rather than concealing the model's limitations. The next chapter on Attribution and Citation examines how to make RAG-grounded answers verifiable, closing the loop from mitigation to accountability.