OpenNLPLab团队近日发布了一项名为Lightning Attention-2的新型线性注意力机制，旨在一劳永逸地解决大语言模型在处理长序列时所面临的问题。该机制让长序列的训练和推理成本与1K序列长度的一致，使得无限长度预训练成为可能。
这项技术突破意味着，在遇到显存瓶颈之前，我们可以无限地增大序列长度，而这对模型训练速度不会产生负面影响。同时，超长文本的推理成本也与1K Tokens的成本一致甚至更少，这将极大地降低当前大语言模型的推理成本。
OpenNLPLab团队的研究成果为无限长度预训练提供了可能，进一步推动了自然语言处理领域的发展。这一创新性突破将为大语言模型的优化和发展带来新的机遇，有望在人工智能领域引发一场革命。
Title: Breakthrough in Attention Mechanism Solves Long Sequence Challenges
Keywords: Attention Mechanism, Long Sequence, Model Optimization
News content: The OpenNLPLab team recently released a new linear attention mechanism called Lightning Attention-2, aimed at once and for all solving the challenges faced by large language models in processing long sequences. This mechanism ensures that the training and inference costs for long sequences are consistent with those of 1K sequence lengths, making infinite-length pre-training a possibility.
This breakthrough means that before encountering memory bottlenecks, we can infinitely increase sequence lengths without any negative impact on model training speed. Additionally, the inference costs for ultra-long texts are also consistent with or even less than the costs of 1K Tokens, greatly reducing the inference costs of current large language models.
The OpenNLPLab team’s research results have made infinite-length pre-training a possibility, further promoting the development of the natural language processing field. This innovative breakthrough will bring new opportunities to the optimization and development of large language models, expected to spark a revolution in the field of artificial intelligence.
【来源】https://www.jiqizhixin.com/articles/2024-01-18-5
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