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Limitations Unveiled: Exploring the Restrictions of Large Language Models

The Promise of LLMs

Large Language Models (LLMs) such as GPT4, Gemini, and Llama, have gained significant attention and recognition worldwide for their groundbreaking achievements in natural language processing. The transformer architecture is a key development which allows LLMs to significantly outperform previous NLP models like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). This architecture enables LLMs to outshine their predecessors by greatly increasing the training dataset and reducing the processing time for natural language tasks. This has led to models that can be trained with billions of parameters while maintaining efficient computation. LLM advancements have shown remarkable proficiency and potential in complex tasks and applications such as sentiment analysis, question answering, and language translation.

While Large Language Models (LLMs) present promising advancements in AI-powered communication, their use in business operations comes with restrictions and limitations that must be carefully considered and undergo rigorous scrutiny before …

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