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LLMs

16   Articles in this Category

The “LLMs” category is dedicated to exploring the fascinating world of large language models and their transformative impact on natural language processing (NLP) and artificial intelligence (AI). Here you’ll find resources that explore the architecture, training, and applications of state-of-the-art language models like GPT, BERT, T5, and their variants. The materials cover the fundamental concepts behind self-attention, transformers, and unsupervised pre-training, and guide you through fine-tuning these models for diverse NLP tasks such as text classification, question answering, summarization, and generation. You’ll learn how to leverage popular libraries like Hugging Face Transformers and spaCy to efficiently work with pre-trained models and build powerful language-based applications. The category also explores advanced topics like few-shot learning, prompt engineering, and model distillation, along with strategies for mitigating bias, ensuring fairness, and enhancing the interpretability of language models. Whether you’re an NLP researcher pushing the boundaries of language understanding or a developer looking to harness the power of LLMs in your projects, these resources will equip you with the knowledge and tools to build cutting-edge language-based AI solutions and stay at the forefront of this rapidly evolving field.

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