Thin Keys, Full Values: Reducing KV Cache via Low-Dimensional Attention Selection

1. 1Identify the different roles of queries, keys, and values in Transformer attention
2. 2Determine the required dimensionality for selection and value transfer
3. 3Apply low-dimensional attention selection to reduce KV cache
4. 4Evaluate the performance of the optimized model

ML researchers and engineers working on Transformer models can benefit from this research to optimize their models' performance and reduce computational costs. This can lead to improved efficiency in natural language processing tasks

💡 Selection in Transformer attention requires only O(log N) dimensions to distinguish among N relevant token categories

🚀 Reduce KV cache in Transformers with low-dimensional attention selection! 💡