Understanding Large Language Models with Jodie Burchell
Mar 13, 2024
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Discover the world of large language models with Dr. Jodie Burchell. Learn about different types of machine learning and where LLMs fit in. Understand their capabilities, limitations, and ethical considerations. Explore the evolution of GPT models and the challenges of scaling and hosting them. Dive into comparisons between GPT3 and GPT4 for language analysis tasks.
Large language models like GPT have revolutionized text processing by encoding vast knowledge with scalability through neural networks.
Fine-tuning models with diverse textual examples and integrating retrieval augmented generation can mitigate inaccuracies and enhance performance.
Deep dives
Evolution of Large Language Models
Large language models have evolved significantly over time, transitioning from earlier symbolic rule-based approaches to current neural net-based models that excel in detecting patterns through vast amounts of data. The advent of transformer models like GPT has revolutionized language processing due to their scalability and ability to encode extensive knowledge from training data, leading to the emergence of powerful generalist natural language processing machines.
Challenges and Considerations
The utilization of large language models presents challenges such as hallucinations where models may generate incorrect or fictional information due to limitations in their training data. To address this, fine-tuning models with factual and non-factual text examples can improve accuracy. Additionally, the integration of retrieval augmented generation (RAG) allows models to access external databases for relevant information, balancing performance with computational intensity.
Practical Applications and Governance
Organizations are exploring the integration of large language models within their operations, balancing trade-offs between model sophistication, computational overhead, and data accessibility. Strategies like using internal databases combined with artificial intelligence coding assistants are being considered to enhance productivity. As businesses navigate the implementation of these models, governance frameworks are crucial to ensure accurate and ethical usage, addressing concerns of data security, ethical considerations, and environmental impact.
Industry Trends and Future Directions
Amidst the growing hype surrounding artificial intelligence technologies, industry investors and practitioners are cautious about the practical outcomes of large language models. The transition from conceptualizations to tangible applications in 2024 marks a pivotal phase in determining the value and feasibility of these technologies. As businesses navigate the landscape of AI adoption, a critical understanding of the capabilities, limitations, and implications of these models is essential for informed decision-making and successful implementation.
What do you know about large language models? While at NDC in London, Richard sat down with Dr. Jodie Burchell to discuss how machine learning has reached this new technological milestone. Jodie talks about different types of machine learning and how large language models fit into the landscape. The conversation explores where LLMs come from, what they are good at, and what they should not be used for. They are not intelligent and certainly not a panacea for work - but they can be valuable when used correctly!