- Sparse Transformers is a technique used in the development of Large Language Models (LLMs). - In this technique, each token in the model attends to only a subset of previous tokens, rather than all tokens, which reduces the computational and memory requirements of the model. - This approach is different from traditional Transformer models, where each token attends to all previous tokens, leading to high computational and memory costs. - Sparse Transformers are designed to mimic the functioning of the human brain, which focuses only on relevant stimuli and ignores irrelevant information. - By attending to only a subset of previous tokens, Sparse Transformers can identify and focus on the most relevant information, leading to better performance and faster training times. - This technique is particularly useful in the development of LLMs, where computational and memory resources are limited, and the model needs to be trained on large amounts of data. - Sparse Transformers have shown promising results in various natural language processing tasks, including language modeling, machine translation, and text classification. - Overall, Sparse Transformers are a powerful tool for developing efficient and effective LLMs, and their architecture-focused approach is congruent with how our brains function. https://arxiv.org/abs/1904.10509