Scaling Multi-Modal Generative AI with Luke Zettlemoyer - #650
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Optimizing Seed Data in Generative AI
This chapter explores the connection between seed data and output quality in generative AI models, emphasizing the efficiency of using a limited number of instruction examples for alignment. It addresses iterative training approaches and the challenges of achieving substantial improvements with each iteration, while also contemplating the reliability of performance evaluations in models with curated versus unaligned datasets. The conversation sheds light on the need for better benchmarks and assessment methods as advancements in AI, like GPT-3, continue to evolve the landscape of natural language processing.
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