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Author

M. S. Shegokar

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Open access Sep 2026

A HALLUCINATION-RESISTANT AGENTIC RAG SYSTEM WITH MULTI-STAGE RETRIEVAL AND RESPONSE VALIDATION

—Large Language Models (LLMs) have shown great abilities in understanding and generating natural language. However, they can create hallucinations by providing factually incorrect or unfounded responses. To tackle this issue, this work suggests a hallucinationresistant Agentic Retrieval-Augmented Generation (RAG) syste...

Shweta Narayan Bamne, M. S. Shegokar · 0 citations
Review Open access Sep 2026

A SURVEY OF HALLUCINATION MITIGATION STRATEGIES IN LARGE LANGUAGE MODELS: FROM DECODING-TIME FIXES TO AGENTIC RETRIEVAL AND VERIFICATION

Hallucination – the generation of fluent but factually unsupported or fabricated content – remains one of the most persistent obstacles to deploying Large Language Models (LLMs) in high-stakes domains such as healthcare, finance, and law. A wide range of mitigation strategies has been proposed, spanning several distinc...

Shweta Narayan Bamne, M. S. Shegokar · 0 citations

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