Enoki: Efficient Multi-Level Hallucination Detection
Researchers introduce Enoki, an open information extraction framework unifying claim-level and span-level hallucination detection in LLMs at lower inference cost.
Enoki extracts text-anchored relational facts, verifies them against evidence, and projects unsupported facts back onto hallucinated spans, so claim-level verification and span-level localization share one representation without separate alignment. It supports LLM-based, encoder-based, and rule-based extraction regimes to balance accuracy against inference cost. Experiments show it stays competitive with strong claim-level systems while using fewer resources and outperforms them on fine-grained span- and entity-level localization. The authors also release EnokiQA, a dual-granularity dataset with aligned claim-level verification and span-level localization annotations.
- Unifies claim-level verification and span-level localization through a shared representation
- Competitive with strong claim-level detectors at lower resource cost
- Supports LLM, encoder, and rule-based extraction regimes
- Releases EnokiQA dual-granularity hallucination benchmark dataset
From the original article
1 min read at huggingface.co
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.
By Elisei Rykov, Timur Ionov, Nikolay Ivanov · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.