Welcome to today's episode, where we're about to embark on an exciting journey into the latest research. In this episode, we'll be delving into a groundbreaking paper that has the potential to revolutionize the field of natural language processing. The paper introduces us to the concept of "Attention Sinks," a novel approach to improving the efficiency of inference with Large Language Models (LLMs) and extending their memory through a Key-Value (KV) Cache.
Traditionally, LLMs have faced challenges when it comes to handling large amounts of data and maintaining contextual information efficiently. However, the concept of Attention Sinks proposes a solution by introducing a mechanism to selectively store and retrieve relevant information during the inference process
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