Unverified50% confidenceSolutionExact time
In actual deployment, to reduce the risk of hallucination, mechanisms such as displaying citation sources, confidence judgment, and manual review usually need to be added.
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Long-term
Relevance
10/14/2026
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UnverifiedCommon engineering techniques for reducing hallucinations include injecting external knowledge via RAG, performing secondary verification of tool call results, setting confidence thresholds to trigger human review, and requiring the model in prompts to say it doesn't know when it doesn't know.76% similarUnverifiedAn auditable output mode that fact-checks high-risk fields after generation is more feasible than preventing hallucinations at the generation stage, and requires no second LLM call.75% similarUnverifiedBy injecting large amounts of domain expertise into the model, fine-tuning can significantly reduce the hallucination rate in specific scenarios74% similarUnverifiedHallucinated proofs differ from rounding errors in numerical computation or obvious error reports in symbolic computation, as the latter two tend to be exposed in subsequent steps, whereas hallucinated proofs may remain undetected until discovered by peer review.74% similarUnverifiedExisting hallucination mitigation approaches (corpus grounding, mutual verification, preference alignment) mostly treat hallucinated terms as generic noise and filter them using consistency heuristics, lacking mechanistic explanations and principled criteria for determining how many expansion terms to retain.73% similar
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