Head Transformer

Head transformers, a key component of large language and vision-language models, are being intensely studied to understand their role in in-context learning and other emergent capabilities. Research focuses on analyzing the training dynamics of these models, particularly the interaction between attention mechanisms (including multi-head attention and induction heads), feed-forward networks, and positional embeddings, often using simplified architectures and synthetic data to gain theoretical insights. These investigations aim to clarify how transformers generalize to unseen data and perform complex tasks, ultimately improving model design and performance in various applications, including natural language processing, computer vision, and multimodal understanding.