Smaller language models are gaining prominence as their capabilities develop in tandem with larger models, offering efficient training and operation on limited resources.
Significant advancements in AI have also addressed AI hallucination, helping refine output accuracy and reliability, which is essential for the development of AI agents.
The iterative relationship between large and small language models indicates a shift in resources and capabilities, making AI technology more accessible and scalable.
In 2024, the emergence of smaller models each with billions of parameters is redefining 'small' while providing effective tools for developers without extensive resources.
#ai-language-models #natural-language-processing #ai-hallucination #machine-learning #ai-development
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