预训练语言模型的参数高效微调方法:批判性审查和评估
IEEE transactions on pattern analysis and machine intelligence
|January 26, 2026
概括
参数高效微调 (PEFT) 方法可以降低大型预训练语言模型 (PLM) 的计算成本. 本综述调查了PEFT技术,为像大型语言模型 (LLM) 这样的模型的高效适应提供了见解.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 预训练语言模型 (PLM),特别是大型语言模型 (LLM),在各种自然语言处理 (NLP) 任务中取得了显著的成功.
- 随着PLM的庞大规模,对下游任务进行微调提出了巨大的计算挑战,特别是在资源有限的情况下.
- 参数高效微调 (PEFT) 已成为通过最小化可训练参数和内存足迹来缓解这些挑战的关键解决方案.
研究的目的:
- 为预训练语言模型 (PLM) 提供参数有效微调 (PEFT) 方法的全面和系统审查.
- 总结现有的PEFT技术,讨论它们的应用,并确定未来的研究方向.
- 为研究人员和从业人员提供与PLM和PEFT工作的实用见解.
主要方法:
- 对参数效率微调 (PEFT) 方法的系统文献综述.
- 各种PEFT方法的分类和总结.
- 对代表性PEFT方法的实验评估,重点关注参数和内存效率.
主要成果:
- PEFT方法显著减少了微调PLM,包括LLM所需的参数和内存的数量.
- 实验结果表明,PEFT在实现与完整微调相匹配的性能方面具有有效性.
- 该研究确定了关键趋势,并提供了对PEFT格局的结构化概述.
结论:
- PEFT是大型预训练语言模型 (PLM) 有效适应的一个重要策略.
- 这项调查是了解和应用资源有限的环境中的PEFT方法的宝贵资源.
- 进一步研究PEFT对于释放PLM在各种NLP应用中的全部潜力至关重要.
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