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Data Brew by Databricks · Nov 21, 2024 · 39 min

Mixed Attention & LLM Context | Data Brew | Episode 35

In this episode, Shashank Rajput, Research Scientist at Mosaic and Databricks, explores innovative approaches in large language models (LLMs), with a focus on Retrieval Augmented Generation (RAG) and its impact on improving efficiency and reducing operational costs. Highlights include: - How RAG enhances LLM accuracy by incorporating relevant external documents. - The evolution of attention mechanisms, including mixed attention strategies. - Practical applications of Mamba architectures and their trade-offs with traditional transformers.

0:00-39:11

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show notes

In this episode, Shashank Rajput, Research Scientist at Mosaic and Databricks, explores innovative approaches in large language models (LLMs), with a focus on Retrieval Augmented Generation (RAG) and its impact on improving efficiency and reducing operational costs.

Highlights include:
- How RAG enhances LLM accuracy by incorporating relevant external documents.
- The evolution of attention mechanisms, including mixed attention strategies.
- Practical applications of Mamba architectures and their trade-offs with traditional transformers.