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上海财经、南方科技和清华大学联合推出针对LLMs的微调方法MiLoRA

上海,中国 – 上海财经大学、南方科技大学和清华大学的研究人员近日联合发布了一种针对大型语言模型(LLMs)的全新微调方法——MiLoRA。该方法旨在通过更新权重分量来矩阵的次要奇异值分解,从而减少计算和内存成本,并提升模型在各种自然语言处理任务中的性能。

MiLoRA的优势

MiLoRA的优势在于其参数高效的微调能力,能够以更少的参数调整来微调大型语言模型,从而减少计算资源的需求。此外,MiLoRA还能够减少在多租户环境中生成新标记时的延迟,并提升模型在各种自然语言处理任务中的性能。

技术原理

MiLoRA的技术原理基于奇异值分解(SVD)将权重矩阵分为主要和次要两部分,主要部分包含重要知识,次要部分包含噪声或长尾信息。在微调过程中,MiLoRA保持主要部分不变,只优化次要部分,在适应新任务的同时保留预训练模型的知识。

MiLoRA还引入了基于输入提示的路由机制,将每个LoRA模块视为一个专家,根据输入提示动态决定激活哪些LoRA专家,从而提高模型的适应性和灵活性。

应用场景

MiLoRA在各种自然语言处理任务中都有广泛的应用场景,包括文本分类、情感分析、问答系统、机器翻译等。此外,MiLoRA还适用于多租户环境、在线内容生成、教育和培训、医疗咨询和诊断等领域。

专家评价

来自上海财经大学的教授李教授表示:“MiLoRA是一个非常有潜力的微调方法,它能够有效地减少计算成本,并提升模型的性能。我们相信MiLoRA将为大型语言模型的应用带来新的突破。”

未来展望

MiLoRA的发布标志着大型语言模型微调技术取得了新的进展。未来,研究人员将继续探索MiLoRA的应用潜力,并将其应用于更多领域,为人工智能的发展做出更大的贡献。

参考文献

  • MiLoRA的arXiv技术论文:https://arxiv.org/pdf/2410.18035

总结

MiLoRA的出现为大型语言模型的微调提供了新的解决方案,它能够有效地减少计算成本,并提升模型的性能。相信MiLoRA将为大型语言模型的应用带来新的突破,并推动人工智能技术的发展。


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