Inside LLMs: How Did They Become Intelligent?

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This event explores the key mechanisms that have allowed Large Language Models (LLMs) to evolve into intelligent systems capable of performing complex language tasks.

Subtopics:

1. The Evolution of LLMs
   Trace the journey of LLMs, from early language models to state-of-the-art systems like GPT and BERT, highlighting key technological advancements that made this possible.
2. Training LLMs – What’s Under the Hood?
   Understand the underlying architecture of LLMs, focusing on how deep learning and neural networks power their ability to learn and generalize language tasks.
3. Self-Attention Mechanism and Transformers
   Learn about the self-attention mechanism, a core feature in transformers, and how it allows LLMs to focus on different parts of the input text for better context understanding.
4. Transfer Learning in LLMs
   Discover how pre-trained LLMs transfer knowledge from vast datasets to new tasks with minimal additional training, revolutionizing NLP performance.
5. Scaling Laws – Bigger Models, Better Results
   Explore the correlation between the size of LLMs and their intelligence, and how increasing model size leads to better understanding and generation capabilities.
6. Challenges and Limitations of LLMs
   Dive into the challenges LLMs face, such as data bias, interpretability, and ethical concerns, as well as ongoing efforts to mitigate these limitations.

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