提出自适应优化方法，提升大模型训练效率与性能
Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training
- 基于自适应优化算法改进训练流程
- 在SQuAD和GLUE上准确率与F1显著提升
- 适合大规模文本与复杂任务的训练场景
随着自然语言处理技术的快速发展，大规模语言模型（LLM）在各类任务中取得了显著成果。然而，如何高效训练这些巨型模型并提升其性能与计算效率仍是重要挑战。本文提出一种改进的自适应优化算法，旨在提高LLM的训练效率与最终性能。在SQuAD和GLUE数据集上的对比实验表明，所提算法在准确率和F1分数上均优于传统优化方法（如SGD、Momentum、AdaGrad、RMSProp和Adam），尤其在处理大规模文本与复杂任务时展现出更强的训练能力。研究结果验证了自适应优化算法在大模型训练中的优势，为未来优化方法提供了新思路。
原文摘要 · Abstract (English)
With the rapid development of natural language processing technology, large-scale language models (LLM) have achieved remarkable results in a variety of tasks. However, how to effectively train these huge models and improve their performance and computational efficiency remains an important challenge. This paper proposes an improved method based on adaptive optimization algorithm, aiming to improve the training efficiency and final performance of LLM. Through comparative experiments on the SQuAD and GLUE data sets, the experimental results show that compared with traditional optimization algorithms (such as SGD, Momentum, AdaGrad, RMSProp and Adam), the adaptive optimization algorithm we proposed has better accuracy and F1 score. Both have achieved significant improvements, especially showed stronger training capabilities when processed large-scale texts and complex tasks. The research results verify the advantages of adaptive optimization algorithms in large-scale language model training and provide new ideas and directions for future optimization methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。