SAC³: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency

Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs). To achieve this goal, we re-examine existing detection approaches based on the self-consistency of LMs and uncover two types of hallucinations resulting from 1) question-level and 2) model-level, which cannot be effectively identified through selfconsistency check alone. Building upon this discovery, we propose a novel sampling-based method, i.e., semantic-aware cross-check consistency (SAC 3 ) that expands on the principle of self-consistency checking. Our SAC 3 approach incorporates additional mechanisms to detect both question-level and model-level hallucinations by leveraging advances including semantically equivalent question perturbation and cross-model response consistency checking. Through extensive and systematic empirical analysis, we demonstrate that SAC 3 outperforms the state of the art in detecting both nonfactual and factual statements across multiple question-answering and open-domain generation benchmarks. 1