Analysis
This article dives deep into the fascinating mathematical principles that underpin the behavior of all Large Language Models (LLMs). It explores how Softmax Crowding and Semantic Drift fundamentally shape how these models process information over space and time, offering critical insights into their limitations and suggesting ways to refine them.
Key Takeaways
- •LLMs are fundamentally limited by Softmax Crowding, where important context gets diluted as input length increases.
- •Semantic Drift causes information to degrade over time, as each generation step introduces potential error.
- •Understanding these limitations is key to developing more robust and reliable Generative AI applications.
Reference / Citation
View Original"The behavior of Generative AI is completely governed by two mathematical laws: 'Softmax Crowding' and 'Semantic Drift'."
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