Hallucination Detection: NLI, Self-Consistency & Learned Models › Summary

Hallucination detection uses four main approaches, each suited to different contexts:

• Entailment-based detection frames the problem as NLI, scoring whether each generated claim is entailed by a source document. The SummaC framework extends this to document level by scoring each generated sentence against each source sentence and taking the maximum. It requires a reference document but is efficient and interpretable, making it the natural choice for retrieval-augmented generation systems.

• Knowledge base verification extracts atomic claims as subject-predicate-object triples and checks them against structured databases like Wikidata. FActScore uses a hybrid approach combining dense retrieval with NLI scoring against Wikipedia passages. It can verify open-domain claims but is limited by knowledge base coverage, temporal gaps, and the cost of triple extraction and entity linking.

• Self-consistency checks exploit the principle that confident, accurate knowledge produces consistent outputs across independent samples. SelfCheckGPT measures the average contradiction score between a claim and multiple sampled passages using NLI, BERTScore, or n-gram overlap. It requires no external reference but is computationally expensive, requires multiple generation passes, and cannot detect confident systematic errors.

• Learned detection models such as FactCC, MiniCheck, and TRUE train dedicated classifiers using synthetic perturbations and human-annotated factuality datasets. They are fast at inference time and transfer across domains, but require careful training data curation to avoid overfitting to specific hallucination patterns. The emerging LLM-as-judge paradigm uses large language models as zero-shot evaluators, trading cost for flexibility.

In practice, the most reliable production systems combine multiple detection signals. A fast entailment check flags responses for which the retrieved context provides insufficient support, a consistency check catches claims that the model appears uncertain about, and periodic offline FActScore audits measure aggregate factuality trends across the system. The next chapter on hallucination mitigation examines how these detection signals can drive active interventions, from retrieval augmentation to training-time corrections.