LLMS are not Knowledge Models! Watch out for AI hallucination!

LLMS are not Knowledge Models! Watch out for AI hallucination!

Who doesn’t love LLM-Powered chatbots? I always say “Use and abuse of LLM chatbots out there, but never trust their facts as hallucinations and made-up information is the norm”. The concept refers to the ability of AI systems to generate convincingly realistic but entirely fabricated information, images, or audio. While the term “hallucination” may evoke images of altered states of consciousness, AI hallucination poses a sobering reality where technology blurs the line between truth and falsehood, raising profound implications for society.

The potential ramifications of AI hallucination are vast and ominous. At the forefront is the specter of misinformation. With AI capable of crafting convincing falsehoods, the spread of disinformation becomes exponentially more potent. Imagine fabricated news articles, forged evidence, or counterfeit audio recordings being disseminated at scale, sowing confusion and discord in society.

Privacy concerns are also a rising problem, as AI-generated content could be used to create lifelike yet entirely fabricated images or videos, implicating individuals in scenarios they never participated in. This not only erodes trust but also threatens the fundamental right to privacy.

Why do AI hallucinations happen?

The reason of AI hallucination lies in the inherent limitations of the technology itself. Despite advancements, AI models, particularly large language models like GPT, are not knowledge models. They lack the discernment to distinguish fact from fiction, relying solely on text patterns in data to generate responses. Consequently, their outputs may not, and generally don’t, align with reality.

Furthermore, biases encoded in the data used to train AI models exacerbate the problem. If the training data is skewed or incomplete, the AI system may inadvertently perpetuate stereotypes or propagate false narratives.

Even Open-AI forums explore the problem.

Is there a way to solve this?

Mitigating the risks associated with AI hallucination demands a multifaceted approach. Users must understand the limitations of AI systems and exercise critical judgment when interpreting their outputs. Enhancing data quality through rigorous vetting and diverse representation can mitigate biases and improve the fidelity of AI-generated content.

Also check out my other article on The problem with developers’ onboarding process… it’s outdated!

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Tiago Marques

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