从软件工程视角系统梳理大模型研发全生命周期挑战与方向
Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead
- 按需求、数据、开发、测试、部署、维护六阶段分析大模型流程
- 指出各阶段核心挑战，如数据质量、评估标准不统一等
- 适合关注大模型工程化落地的研究者与开发者参考
大语言模型（LLMs）的快速发展重塑了人工智能格局，推动了学术与产业的无限可能。然而，其研发过程面临日益复杂的全生命周期挑战，现有研究尚未从软件工程（SE）视角系统梳理这些问题与解决方案。本文系统分析了大模型开发全生命周期的六个阶段：需求工程、数据集构建、模型开发与优化、测试与评估、部署与运维、维护与演进。针对每个阶段，识别关键挑战并提出潜在研究方向。整体上，本文从软件工程角度为未来大模型发展提供了重要洞察。
原文摘要 · Abstract (English)
The rapid advancement of large language models (LLMs) has redefined artificial intelligence (AI), pushing the boundaries of AI research and enabling unbounded possibilities for both academia and the industry. However, LLM development faces increasingly complex challenges throughout its lifecycle, yet no existing research systematically explores these challenges and solutions from the perspective of software engineering (SE) approaches. To fill the gap, we systematically analyze research status throughout the LLM development lifecycle, divided into six phases: requirements engineering, dataset construction, model development and enhancement, testing and evaluation, deployment and operations, and maintenance and evolution. We then conclude by identifying the key challenges for each phase and presenting potential research directions to address these challenges. In general, we provide valuable insights from an SE perspective to facilitate future advances in LLM development.
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