大型语言模型之路:源头,挑战和未来的前景
Wei Zhao1, Xin Yang1, Zhihan Lyu1
1School of Computer Science and Technology, Xidian University, Xi'an, China.
Research (Washington, D.C.)
|July 30, 2025
概括
大型语言模型 (LLM) 显示出令人印象深刻的功能,但面临着现实世界的挑战. 这篇论文回顾了LLM的发展,重点关注新兴能力,人类对齐,检索增强生成和领域应用.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 语言模型 (LMs) 是人工智能的基础,最近的进展导致了大型语言模型 (LLMs).
- 在使用变压器架构的广泛数据集上进行预训练的LLM显示出显著的零射击和少数射击学习能力.
- 尽管取得了成功,但目前的LLM仍在努力解决复杂的现实世界问题.
研究的目的:
- 提供语言模型发展的历史概述.
- 讨论LLM研究当前的挑战和未来的方向.
- 探索LLM的关键方面:新兴能力,人类对齐,检索增强生成和特定领域的应用.
主要方法:
- 关于语言模型和大型语言模型的现有文献的审查.
- 基于变压器的预训练方法的分析.
- 专注于特定的LLM特征,包括新兴能力,人类对齐,RAG和应用.
主要成果:
- 在各种任务中,LLM表现出了显著的表现,引发了广泛的兴趣.
- 像DeepSeek信号这样的模型的出现继续在该领域的快速进展.
- 进步的关键领域包括增强新兴能力,确保人类对齐,并优化提取增强生成.
结论:
- 法律法学代表了人工智能的重大飞跃,在各个领域都有巨大的潜力.
- 解决现实世界的适用性目前的局限性对于未来的LLM开发至关重要.
- 对新兴能力,人类调整,RAG和专业应用的持续研究将塑造LLMs的未来.
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