Chunk Wise

Chunk-wise processing addresses the limitations of large language models (LLMs) and other deep learning models when handling long sequences, aiming to improve efficiency and performance without sacrificing accuracy. Current research focuses on optimizing chunking strategies, including adaptive and late chunking methods, and integrating these with various architectures like transformers and recurrent neural networks, often incorporating techniques such as graph-based representations and attention mechanisms to maintain contextual information across chunks. These advancements are significant for improving the scalability and applicability of LLMs in diverse fields, such as question answering, machine translation, and speech recognition, enabling the processing of significantly longer inputs than previously possible.