Data Brew by Databricks Mixed Attention & LLM Context | Data Brew | Episode 35
Nov 21, 2024
Shashank Rajput, a Research Scientist specializing in large language models at Mosaic and Databricks, dives into innovative techniques like Retrieval Augmented Generation (RAG) to boost LLM efficiency. He discusses how RAG improves LLM accuracy using external documents. The conversation covers the evolution of attention mechanisms, particularly mixed strategies. They also explore the Mamba architecture, showcasing its speed and memory management compared to traditional transformers, highlighting practical applications and efficiency trade-offs.
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How LLMs Process Text
- LLMs process words using feedforward networks (FFNs) for individual tokens and attention mechanisms for relationships between tokens.
- Attention creates key/value vectors for each word and a query vector for the current token to assess importance within the sequence.
Standard Attention Drawbacks
- Standard attention's strength and weakness is its ability to consider all tokens in a sequence, which is computationally expensive.
- This comprehensive view is often unnecessary, as predicting the next word typically relies on a smaller, recent context.
Mixed Attention Explained
- Mixed attention combines sliding window attention, focusing on recent tokens, with full attention, considering all previous tokens.
- This approach improves efficiency and speed while retaining accuracy for longer contexts by adding a few full attention layers.