Grounded in Context: Retrieval-Based Method for Hallucination Detection

• URL: http://arxiv.org/abs/2504.15771v1
• Date: Tue, 22 Apr 2025 10:28:23 GMT
• Title: Grounded in Context: Retrieval-Based Method for Hallucination Detection
• Authors: Assaf Gerner, Netta Madvil, Nadav Barak, Alex Zaikman, Jonatan Liberman, Liron Hamra, Rotem Brazilay, Shay Tsadok, Yaron Friedman, Neal Harow, Noam Bresler, Shir Chorev, Philip Tannor,
• Abstract summary: We present "Grounded in Context" - Deepchecks' hallucination detection framework.<n>Inspired by RAG architecture, our method integrates retrieval and Natural Language Inference (NLI) models to predict factual consistency.<n>Our framework identifies unsupported claims with an F1 score of 0.83 in RAGTruth's response-level classification task.
• Score: 2.0483545062606976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite advancements in grounded content generation, production Large Language Models (LLMs) based applications still suffer from hallucinated answers. We present "Grounded in Context" - Deepchecks' hallucination detection framework, 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.

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