Salesforce AI Introduces ‘ThinK’: A New AI Method that Exploits Substantial Redundancy Across the Channel Dimension of the KV Cache
Large Language Models (LLMs) have revolutionized natural language processing, demonstrating exceptional performance across various tasks. The Scaling Law suggests that as model size increases, LLMs develop emergent abilities, enhancing their context understanding and long sequence handling capabilities. This growth enables LLMs to generate coherent responses and power applications like document summarization, code generation, and conversational AI. However, LLMs face significant challenges in terms of cost and efficiency. The expenses associated with LLM generation escalate with increasing model size and sequence length, affecting both the training and inference stages. Additionally, managing long sequences presents computational burdens due to the quadratic complexity of the transformer attention mechanism, which scales poorly with sequence length. These challenges necessitate the development of efficient LLM architectures and strategies to reduce memory consumption, particularly in long-context scenarios.
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