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The Data Stack Show
205: How to make LLMs Boring (Predictable, Reliable, and Safe), Featuring Nicolay Gerold
Sep 4, 2024
Nicolay Gerold, an expert in artificial intelligence, shares his journey in developing large language models (LLMs) while discussing the challenges of generating reliable outputs. He emphasizes the importance of data quality over model adjustments, addressing the barriers to effective LLM use. The conversation also highlights the need for human oversight in AI applications, especially regarding security and customer interactions. Nicolay delves into monitoring, testing challenges, and the evolving landscape of AI startups, offering insights into making LLMs more predictable and reliable.
48:52
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Quick takeaways
- The significance of a data-centric AI approach highlights the shift from model tuning to enhancing dataset quality for improved performance.
- Addressing personalization barriers in LLMs requires effective fine-tuning with synthetic data to cater to individual user preferences.
Deep dives
The Evolution of Large Language Models (LLMs)
Large Language Models have progressed significantly since their inception, moving from generating nonsensical outputs to producing coherent text. Initially, when LLMs were first implemented, the usability ratio was low, with only about one in ten attempts yielding satisfactory results. However, advancements such as the introduction of attention mechanisms and reinforcement learning from human feedback have enhanced their capability to follow instructions and align with user preferences. This evolution emphasizes the critical challenge of making LLMs predictable and reliable for practical applications, rather than simply pushing boundaries of creativity.
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