近日,OpenNLPLab 团队在其研究中取得了重大突破,发布了一款名为 Lightning Attention-2 的新一代注意力机制,旨在彻底解决大语言模型在处理长序列时面临的挑战。据机器之心报道,这一创新技术开源后,将为人工智能领域的研究和应用带来深远影响。
OpenNLPLab 的 Lightning Attention-2 机制以线性复杂度为核心,实现了训练和推理成本与序列长度为1K时相当的壮举。这意味着,在显存允许的范围内,研究人员可以无限制地增加序列长度,而不会对模型训练速度造成显著影响。这一突破为大语言模型的无限长度预训练开辟了新的可能,极大地扩展了模型处理复杂语言任务的能力。
此外,Lightning Attention-2 还在超长文本的推理效率上取得了突破,其成本甚至低于处理1K Tokens 的水平。这一优化将显著降低当前大语言模型的运行成本,为大规模文本处理和分析提供更加经济高效的解决方案。
OpenNLPLab 团队的这一创新成果,无疑将推动自然语言处理技术的进步,为新闻编辑、智能客服、机器翻译等领域带来更高的效率和准确性,进一步推动人工智能与实际应用的深度融合。
英语如下:
**News Title:** “OpenNLPLab Unveils Innovative Mechanism: Lightning Attention-2, Pioneering a New Era of Unlimited Sequence Length Pre-training”
**Keywords:** OpenNLPLab, Lightning Attention-2, Long Sequence Processing
**News Content:**
OpenNLPLab recently announced a groundbreaking advancement in its research with the launch of Lightning Attention-2, a next-generation attention mechanism designed to tackle the challenges faced by large language models when processing lengthy sequences. As reported by AI之心, this innovative technology, once open-sourced, is set to have a profound impact on research and applications within the artificial intelligence domain.
At the core of OpenNLPLab’s Lightning Attention-2 lies linear complexity, enabling it to perform training and inference with costs equivalent to handling sequences of 1K length. This breakthrough allows researchers, within the constraints of available memory, to increase sequence lengths without significantly affecting model training speed. Consequently, it paves the way for unlimited-length pre-training of large language models, greatly enhancing their capacity to handle complex language tasks.
Furthermore, Lightning Attention-2 achieves a breakthrough in inference efficiency for ultra-long texts, with costs even lower than processing 1K tokens. This optimization reduces the operational cost of current large language models, offering more cost-effective solutions for large-scale text processing and analysis.
OpenNLPLab’s pioneering achievement is poised to propel advancements in natural language processing, boosting efficiency and accuracy in fields such as news editing, intelligent customer service, and machine translation, thereby fostering a deeper integration of artificial intelligence with practical applications.
【来源】https://www.jiqizhixin.com/articles/2024-01-18-5
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