大型语言模型和联合学习的整合
Chaochao Chen1, Xiaohua Feng1, Yuyuan Li1,2
1Zhejiang University, Hangzhou, China.
Patterns (New York, N.Y.)
|January 8, 2025
概括
大型语言模型 (LLM) 和联合学习 (FL) 越来越多地被整合在一起,以解决数据稀缺问题. 本综述探讨了它们在医疗保健和金融等关键领域的综合潜力和应用.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 大型语言模型 (LLM) 的参数大小不断扩大,需要高质量的数据,从而造成数据稀缺的挑战.
- 联合学习 (FL) 提供了一种保护隐私的方法,用于在分散数据上的培训模型.
- 在任务概括和在数据隐私中LLM和FL的固有优势创造了一个协同研究机会.
研究的目的:
- 全面审查LLMs和FL的整合.
- 为理解LLM和FL子技术的融合提出一个框架.
- 探索这个跨学科领域的实际应用和未来的研究方向.
主要方法:
- 对现有关于LLM和FL整合的研究进行系统审查.
- 开发一个三部分的研究框架:LLM副技术与FL,FL副技术与LLM,和整体合并.
- 分析典型的应用,优势,挑战和未来方向.
主要成果:
- 确定了对LLMs和FL的互补性日益增长的研究兴趣.
- 详细介绍了一个研究框架,对整合方法进行分类.
- 总结了合并LLM-FL系统的当前应用,好处和挑战.
结论:
- 整合LLMs和FL为克服AI数据限制提供了一个有希望的途径.
- 需要进一步的研究来应对挑战,并释放这种协同作用的全部潜力.
- 在医疗保健,金融和教育领域的实际应用突出显示了联合LLM-FL方法的变革性影响.
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