| title | Enoki: Efficient Multi-Level Hallucination Detection |
|---|---|
| authors | Elisei Rykov, Timur Ionov, Nikolay Ivanov, Maksim Savkin, Maksim Makarenko, Alexander Panchenko, Vasily Konovalov, Julia Belikova |
| date | 2026-09-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.00581 |
| affiliation | s-nlp |
| upvotes | 27 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@misc{rykov2026enoki,
title={Enoki: Efficient Multi-Level Hallucination Detection},
author={Elisei Rykov and Timur Ionov and Nikolay Ivanov and Maksim Savkin and Maksim Makarenko and Alexander Panchenko and Vasily Konovalov and Julia Belikova},
year={2026},
eprint={2609.00581},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.00581}
}