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Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation

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arxiv 2410.14262 v3 pith:VPNM5M6H submitted 2024-10-18 cs.CR cs.CL

classification cs.CRcs.CL
keywords accuracyadvancedagentagentsartistcontenthallucinationsmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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This study explores the ability of Large Language Model (LLM) agents to detect and correct hallucinations in AI-generated content. A primary agent was tasked with creating a blog about a fictional Danish artist named Flipfloppidy, which was then reviewed by another agent for factual inaccuracies. Most LLMs hallucinated the existence of this artist. Across 4,900 test runs involving various combinations of primary and reviewing agents, advanced AI models such as Llama3-70b and GPT-4 variants demonstrated near-perfect accuracy in identifying hallucinations and successfully revised outputs in 85% to 100% of cases following feedback. These findings underscore the potential of advanced AI models to significantly enhance the accuracy and reliability of generated content, providing a promising approach to improving AI workflow orchestration.

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Cited by 1 Pith paper

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  1. Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks

    cs.CL 2025-01 reject novelty 5.0 of 10

    A three-agent pipeline with OVON JSON messages lowers the authors' disclaimer-based hallucination score on 310 prompts, but that score does not measure truth.

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