Paper
Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval
Authors
Md. Asraful Haque, Aasar Mehdi, Maaz Mahboob, Tamkeen Fatima
Abstract
Large Language Models (LLMs) have achieved unprecedented fluency but remain susceptible to "hallucinations" - the generation of factually incorrect or ungrounded content. This limitation is particularly critical in high-stakes domains where reliability is paramount. We propose a domain-grounded tiered retrieval and verification architecture designed to systematically intercept factual inaccuracies by shifting LLMs from stochastic pattern-matchers to verified truth-seekers. The proposed framework utilizes a four-phase, self-regulating pipeline implemented via LangGraph: (I) Intrinsic Verification with Early-Exit logic to optimize compute, (II) Adaptive Search Routing utilizing a Domain Detector to target subject-specific archives, (III) Corrective Document Grading (CRAG) to filter irrelevant context, and (IV) Extrinsic Regeneration followed by atomic claim-level verification. The system was evaluated across 650 queries from five diverse benchmarks: TimeQA v2, FreshQA v2, HaluEval General, MMLU Global Facts, and TruthfulQA. Empirical results demonstrate that the pipeline consistently outperforms zero-shot baselines across all environments. Win rates peaked at 83.7% in TimeQA v2 and 78.0% in MMLU Global Facts, confirming high efficacy in domains requiring granular temporal and numerical precision. Groundedness scores remained robustly stable between 78.8% and 86.4% across factual-answer rows. While the architecture provides a robust fail-safe for misinformation, a persistent failure mode of "False-Premise Overclaiming" was identified. These findings provide a detailed empirical characterization of multi-stage RAG behavior and suggest that future work should prioritize pre-retrieval "answerability" nodes to further bridge the reliability gap in conversational AI.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.17872v1</id>\n <title>Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval</title>\n <updated>2026-03-18T15:59:30Z</updated>\n <link href='https://arxiv.org/abs/2603.17872v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.17872v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Large Language Models (LLMs) have achieved unprecedented fluency but remain susceptible to \"hallucinations\" - the generation of factually incorrect or ungrounded content. This limitation is particularly critical in high-stakes domains where reliability is paramount. We propose a domain-grounded tiered retrieval and verification architecture designed to systematically intercept factual inaccuracies by shifting LLMs from stochastic pattern-matchers to verified truth-seekers. The proposed framework utilizes a four-phase, self-regulating pipeline implemented via LangGraph: (I) Intrinsic Verification with Early-Exit logic to optimize compute, (II) Adaptive Search Routing utilizing a Domain Detector to target subject-specific archives, (III) Corrective Document Grading (CRAG) to filter irrelevant context, and (IV) Extrinsic Regeneration followed by atomic claim-level verification. The system was evaluated across 650 queries from five diverse benchmarks: TimeQA v2, FreshQA v2, HaluEval General, MMLU Global Facts, and TruthfulQA. Empirical results demonstrate that the pipeline consistently outperforms zero-shot baselines across all environments. Win rates peaked at 83.7% in TimeQA v2 and 78.0% in MMLU Global Facts, confirming high efficacy in domains requiring granular temporal and numerical precision. Groundedness scores remained robustly stable between 78.8% and 86.4% across factual-answer rows. While the architecture provides a robust fail-safe for misinformation, a persistent failure mode of \"False-Premise Overclaiming\" was identified. These findings provide a detailed empirical characterization of multi-stage RAG behavior and suggest that future work should prioritize pre-retrieval \"answerability\" nodes to further bridge the reliability gap in conversational AI.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-03-18T15:59:30Z</published>\n <arxiv:comment>14 Pages, 5 Figures, 4 Tables</arxiv:comment>\n <arxiv:primary_category term='cs.CL'/>\n <author>\n <name>Md. Asraful Haque</name>\n </author>\n <author>\n <name>Aasar Mehdi</name>\n </author>\n <author>\n <name>Maaz Mahboob</name>\n </author>\n <author>\n <name>Tamkeen Fatima</name>\n </author>\n </entry>"
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