This is what LLMs really store
Published:Dec 27, 2025 13:01
•1 min read
•Machine Learning Street Talk
Analysis
The article, originating from Machine Learning Street Talk, likely delves into the inner workings of Large Language Models (LLMs) and what kind of information they retain. Without the full content, it's difficult to provide a comprehensive analysis. However, the title suggests a focus on the actual data structures and representations used within LLMs, moving beyond a simple understanding of them as black boxes. It could explore topics like the distribution of weights, the encoding of knowledge, or the emergent properties that arise from the training process. Understanding what LLMs truly store is crucial for improving their performance, interpretability, and control.
Key Takeaways
- •LLMs store information in complex ways.
- •Understanding storage is key to improvement.
- •Interpretability is linked to storage knowledge.
Reference
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