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Mitigating Hallucination in LLMs by yasirahmadmalik8 is a document available to read on EtoBox.

This paper explores hallucination in Large Language Models (LLMs), defining intrinsic and extrinsic hallucinations and proposing a framework to quantify hallucination risk. It reviews detection strategies, such as uncertainty estimation and attention checks, and mitigation techniques including retrieval-augmented generation and fact verification. The work emphasizes the need for theoretical analysis and practical guidelines to address hallucination challenges in LLMs, while also outlining evaluation protoco

Author

yasirahmadmalik8

Language

EN