系统梳理大模型发展现状与社会影响，指明未来研究方向。
Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions
- 综述性分析大模型研究主题与技术路径
- 揭示伦理挑战与社会影响的深层问题
- 适合关注AI治理与负责任发展的研究者
近年来，大语言模型（LLMs）在自然语言处理（NLP）和人工智能（AI）领域取得迅猛进展，显著推动了人机语言理解与交互能力的提升。本文通过系统性文献调研，识别出大模型发展的核心主题、演进方向及其局限性。研究涵盖负责任开发、算法优化、伦理挑战与社会影响等关键议题。结果阐明了当前大模型研究的目标、方法、瓶颈及未来趋势，为后续研究提供全面参考。文章特别强调了有望带来积极社会影响的应用方向，同时指出亟需重视的伦理与治理问题。
原文摘要 · Abstract (English)
There have been rapid advancements in the capabilities of large language models (LLMs) in recent years, greatly revolutionizing the field of natural language processing (NLP) and artificial intelligence (AI) to understand and interact with human language. Therefore, in this work, we conduct a systematic investigation of the literature to identify the prominent themes and directions of LLM developments, impacts, and limitations. Our findings illustrate the aims, methodologies, limitations, and future directions of LLM research. It includes responsible development considerations, algorithmic improvements, ethical challenges, and societal implications of LLM development. Overall, this paper provides a rigorous and comprehensive overview of current research in LLM and identifies potential directions for future development. The article highlights the application areas that could have a positive impact on society along with the ethical considerations.
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。