Author(s): Kaushik Rajan

Originally published on Towards AI.

A deep dive into the research that explains why AI hallucinations are an inherent feature of Large Language Models, not just a bug.

You’ve probably seen it before. You ask an AI chatbot a simple question, and it confidently spits out an answer that sounds plausible but is completely, utterly wrong. It might invent a historical event, fabricate a quote, or even create a fake academic paper. This phenomenon, known as “hallucination,” is one of the most significant and stubborn problems facing modern artificial intelligence.

The article discusses the phenomenon of AI hallucinations, highlighting their nature as an inherent feature of large language models rather than a mere bug. It explores the distinction between imitation and validation errors, explaining that AIs generate plausible yet incorrect information due to their design as pattern-matching engines that prioritize fluency and statistical likelihood. The implications span various domains, such as medicine and law, raising concerns about their reliability and the need for critical engagement with AI-generated content.

Read the full blog for free on Medium.

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Published via Towards AI