Enoki: Efficient Multi-Level Hallucination Detection

• URL: http://arxiv.org/abs/2609.00581v2
• Date: Fri, 04 Sep 2026 09:03:38 GMT
• Title: Enoki: Efficient Multi-Level Hallucination Detection
• Abstract summary: We propose Enoki, an Open Information Extraction framework for hallucination detection.<n>Enoki extracts text-anchored relational facts, verifies them against evidence, and projects unsupported facts back to hallucinated spans.<n>We show that Enoki remains competitive with strong claim-level systems while using fewer resources and achieves superior performance on fine-grained span- and entity-level localization.
• Score: 48.671558015589575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucination detectors usually operate at a single level: claim-level methods provide interpretable factual units, while span-level methods localize unsupported text. Bridging these views is costly, as LLM-heavy pipelines require multiple decomposition and verification calls, and modular systems need additional claim-to-span alignment. We propose Enoki, an Open Information Extraction framework for multi-level hallucination detection. Enoki extracts text-anchored relational facts, verifies them against evidence, and projects unsupported facts back to hallucinated spans. This shared representation enables claim-level verification and span-level localization without requiring separate alignment. Enoki supports LLM-based, encoder-based, and rule-based extraction regimes, balancing accuracy and inference cost through a common interface. Experiments show that Enoki remains competitive with strong claim-level systems while using fewer resources and achieves superior performance on fine-grained span- and entity-level localization. We also release EnokiQA, a dual-granularity dataset with aligned claim-level verification and span-level localization annotations.

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