What are large language models (LLMs)?

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This is a podcast episode titled, What are large language models (LLMs)?. The summary for this episode is: This episode of Techsplainers explores large language models (LLMs), the powerful AI systems revolutionizing how we interact with technology through human language. We break down how these massive statistical prediction machines are built on transformer architecture, enabling them to understand context and relationships between words far better than previous systems. The podcast walks through the complete development process—from pretraining on trillions of words and tokenization to self-supervised learning and the crucial self-attention mechanism that allows LLMs to capture linguistic relationships. We examine various fine-tuning methods, including supervised fine-tuning, reinforcement learning from human feedback (RLHF), and instruction tuning, that help adapt these models for specific uses. The discussion covers practical aspects like prompt engineering, temperature settings, context windows, and retrieval augmented generation (RAG) while showcasing real-world applications across industries. Finally, we address the significant challenges of LLMs, including hallucinations, biases, and resource demands, alongside governance frameworks and evaluation techniques used to ensure these powerful tools are deployed responsibly. Find more information at <a href="https://www.ibm.com/think/podcasts/techsplainers" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers</a> Narrated by Amanda Downie

DESCRIPTION

This episode of Techsplainers explores large language models (LLMs), the powerful AI systems revolutionizing how we interact with technology through human language. We break down how these massive statistical prediction machines are built on transformer architecture, enabling them to understand context and relationships between words far better than previous systems. The podcast walks through the complete development process—from pretraining on trillions of words and tokenization to self-supervised learning and the crucial self-attention mechanism that allows LLMs to capture linguistic relationships. We examine various fine-tuning methods, including supervised fine-tuning, reinforcement learning from human feedback (RLHF), and instruction tuning, that help adapt these models for specific uses. The discussion covers practical aspects like prompt engineering, temperature settings, context windows, and retrieval augmented generation (RAG) while showcasing real-world applications across industries. Finally, we address the significant challenges of LLMs, including hallucinations, biases, and resource demands, alongside governance frameworks and evaluation techniques used to ensure these powerful tools are deployed responsibly.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Amanda Downie