研究约 1 分钟阅读最近更新
DomainPilot：基于领域级损失引导的两阶段数据混合优化以高效微调语言模型DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-Tuning
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
研究人员推出了一种名为DomainPilot的新框架，旨在优化大型语言模型的微调过程。这种两阶段数据混合优化方法利用领域级损失信号来提升模型性能。值得注意的是，DomainPilot在不增加数据量或相关成本的情况下实现了这些改进，使其成为微调大型语言模型的经济高效解决方案。
Researchers have unveiled a novel framework called DomainPilot, designed to optimize the fine-tuning process of large language models. This two-stage data mixture optimization approach utilizes domain-level loss signals to enhance model performance. Notably, DomainPilot achieves these improvements without necessitating an increase in data volume or associated costs, making it a cost-effective solution for fine-tuning large language models.
要点
DomainPilot引入了一个用于语言模型微调的两阶段优化框架。
DomainPilot introduces a two-stage optimization framework for language model fine-tuning.
该方法利用领域级损失信号以提高性能。
The method leverages domain-level loss signals for improved performance.
在没有额外数据或成本的情况下可以实现显著的性能提升。
Significant performance gains can be achieved without additional data or costs.
为何重要
DomainPilot的推出代表了语言模型微调效率的重大进步，这对于自然语言处理中的各种应用至关重要。这种方法可以带来更易获取和经济高效的AI解决方案。
The introduction of DomainPilot represents a significant advancement in the efficiency of language model fine-tuning, which is crucial for various applications in natural language processing. This approach can lead to more accessible and cost-effective AI solutions.
产品经理
对于产品经理而言，DomainPilot提供了一条增强产品能力的途径，而无需承担额外的数据成本，从而实现语言模型在产品中的更高效部署。
For product managers, DomainPilot offers a pathway to enhance product capabilities without incurring additional data costs, enabling more efficient deployment of language models in products.
开发者
开发人员和工程师可以利用DomainPilot实施更有效的微调策略，在保持资源效率的同时提升模型性能。
Developers and engineers can leverage DomainPilot to implement more effective fine-tuning strategies, improving model performance while maintaining resource efficiency.
学生与求职者
学生和求职者可以通过了解DomainPilot受益，因为它突出了AI研究中的创新方法，提供了对高效模型训练技术的洞察，这在就业市场中越来越相关。
Students and job-seekers can benefit from understanding DomainPilot as it highlights innovative approaches in AI research, providing insights into efficient model training techniques that are increasingly relevant in the job market.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.LG
DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-TuningResearchers introduced DomainPilot, a two-stage data mixture optimization framework that enhances the fine-tuning of large language models by leveraging domain-level loss signals, achieving significant performance improvements without increasing data volume or costs.