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In the field of artificial intelligence, the DeepSeek team recently released their latest research成果, introducing an innovative sparse attention mechanism named NSA (Native Sparse Attention). The core objective of this technology is to enhance the speed of long context training and inference, particularly optimized for modern hardware, significantly improving the efficiency of training and inference. The launch of NSA technology brings significant changes to the training of artificial intelligence models. Firstly, it utilizes a series of advancements tailored for modern computational hardware.

Recently, large language models (LLMs) with ultra-long context windows have become a hot topic of discussion. These models are capable of processing hundreds of thousands or even millions of tokens in a single prompt, opening up many new possibilities for developers. However, how well can these long context LLMs understand and utilize the vast information they receive? To address this question, researchers at Google DeepMind have launched a new benchmark called Michelangelo, aimed at evaluating long context reasoning capabilities. The research findings indicate that...

Beijing ZhiYuan Artificial Intelligence Research Institute, in collaboration with Renmin University of China's Gaoqing School of Artificial Intelligence, has launched an innovative AI model framework called MemoRAG. This framework, based on long-term memory, aims to advance Retrieval-Augmented Generation (RAG) technology to handle more complex tasks beyond simple question answering.

Zhipu AI announces that the LLM GLM-4-Long, which supports ultra-long context lengths, is now available on the open platform bigmodel.cn. This model is designed specifically for processing long texts and can read the equivalent of two copies of 'Dream of the Red Chamber' or 125 papers in one go. It is widely applied in scenarios such as translating long documents, global analysis of financial reports, extracting key information, and building chatbots with ultra-long memory.

Long context understanding is a key challenge in the field of natural language processing, especially when large language models (LLMs) handle text that exceeds their context window size. To address this issue, researchers developed the LooGLE benchmark test, aimed at assessing LLMs' capability to understand extremely long documents (averaging 19.3k words, comprising 776 articles across multiple domains). LooGLE includes 7 tasks that cover both short and long dependencies, evaluating model understanding across texts of varying lengths. The test data is sourced from open-access content after 2022.