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Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks

arxiv.org · 10,402 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study investigates how orchestrating multiple specialized Artificial Intelligent Agents can help mitigate such hallucinations, with a focus on systems leveraging Natural Language Processing (NLP) to facilitate seamless agent interactions. To achieve this, we design a pipeline that introduces over three hundred prompts, purposefully crafted to induce hallucinations, into a front-end agent. The outputs are then systematically reviewed and refined by second- and third-level agents, each employing distinct large language models and tailored strategies to detect unverified claims, incorporate explicit disclaimers, and clar

Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks Diego Gosmar Chief AI Officer XCALLY Open Voice Interoperability Initiative Member Linux Foundation AI & Data Torino, TO 10100, Italy diego.gosmar@ieee.org & Deborah A. Dahl Principal Conversational Technologies Open Voice Interoperability Initiative Member Linux Foundation AI & Data Plymouth Meeting, Pennsylvania, USA dahl@conversational-technologies.com Lead Author Abstract Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study

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