Multi-Token Attention: Going Beyond Single-Token Focus in Transformers

Multi-Token Attention (MTA) enhances transformer models by enabling focus on token groups simultaneously, improving performance on complex pattern recognition and long-range information retrieval tasks. It introduces convolutions over query-key pairs and across attention heads, allowing more effective multi-token pattern detection. While MTA shows promise for boosting accuracy without significantly increasing model size, it faces computational overhead and implementation challenges compared to standard attention mechanisms.