ORION Grounded in Context: Retrieval-Based Method for Hallucination Detection
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915297539129344 |
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| author | Gerner, Assaf Madvil, Netta Barak, Nadav Zaikman, Alex Liberman, Jonatan Hamra, Liron Brazilay, Rotem Tsadok, Shay Friedman, Yaron Harow, Neal Bressler, Noam Chorev, Shir Tannor, Philip |
| author_facet | Gerner, Assaf Madvil, Netta Barak, Nadav Zaikman, Alex Liberman, Jonatan Hamra, Liron Brazilay, Rotem Tsadok, Shay Friedman, Yaron Harow, Neal Bressler, Noam Chorev, Shir Tannor, Philip |
| contents | Despite advancements in grounded content generation, production Large Language Models (LLMs) based applications still suffer from hallucinated answers. We present "Grounded in Context" - a member of Deepchecks' ORION (Output Reasoning-based InspectiON) family of lightweight evaluation models. It is our framework for hallucination detection, designed for production-scale long-context data and tailored to diverse use cases, including summarization, data extraction, and RAG. Inspired by RAG architecture, our method integrates retrieval and Natural Language Inference (NLI) models to predict factual consistency between premises and hypotheses using an encoder-based model with only a 512-token context window. Our framework identifies unsupported claims with an F1 score of 0.83 in RAGTruth's response-level classification task, matching methods that trained on the dataset, and outperforming all comparable frameworks using similar-sized models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15771 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ORION Grounded in Context: Retrieval-Based Method for Hallucination Detection Gerner, Assaf Madvil, Netta Barak, Nadav Zaikman, Alex Liberman, Jonatan Hamra, Liron Brazilay, Rotem Tsadok, Shay Friedman, Yaron Harow, Neal Bressler, Noam Chorev, Shir Tannor, Philip Machine Learning Despite advancements in grounded content generation, production Large Language Models (LLMs) based applications still suffer from hallucinated answers. We present "Grounded in Context" - a member of Deepchecks' ORION (Output Reasoning-based InspectiON) family of lightweight evaluation models. It is our framework for hallucination detection, designed for production-scale long-context data and tailored to diverse use cases, including summarization, data extraction, and RAG. Inspired by RAG architecture, our method integrates retrieval and Natural Language Inference (NLI) models to predict factual consistency between premises and hypotheses using an encoder-based model with only a 512-token context window. Our framework identifies unsupported claims with an F1 score of 0.83 in RAGTruth's response-level classification task, matching methods that trained on the dataset, and outperforming all comparable frameworks using similar-sized models. |
| title | ORION Grounded in Context: Retrieval-Based Method for Hallucination Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.15771 |