"ChatGPT and the other GenAI chatbots that have taken the tech world by storm are all trained on massive datasets comprising trillions of data points. The algorithms that dictate how large language models (LLMs) process queries and spit out responses often involve billions of logic nodes. It's the enormity of these training datasets and algorithms that give an LLM its power, but it seems like we might have hit the ceiling when it comes to feeding GenAI."
"Bigger is no longer automatically better. Instead, it's time to get smarter, as Sam Altman, CEO of OpenAI, acknowledged recently. "I think we're at the end of the era where it's going to be these, like, giant, giant models," he said, adding, "We'll make them better in other ways." Today there is arguably more momentum around specialized LLMs that are trained only on industry-specific, company-specific, and even personal data."
Massive general LLMs have relied on trillions of data points and billions of parameters, but scale is reaching diminishing returns. Further improvements will come from smarter design rather than simply increasing size. Specialized LLMs trained on industry-specific, company-specific, or personal data use smaller datasets but offer greater relevance and precision for targeted use cases. Enterprises are increasingly building private models to reduce data-leak risk and improve security. Specialized models reduce hallucinations and bias by excluding irrelevant information and are more likely to produce accurate, trustworthy answers in high-risk domains like healthcare.
Read at Techzine Global
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