智能机器人创新与开发实践课是一项技术性较强的课程,加强学生对机器人和人工智能的认识与应用,经过本门课程的探索发现在实际教学中需要理论与实践的结合,基于最新的控制理论信息物理系统(Cyber-Physical Systems)作为理论基础,引入国家级竞赛场景为实践方式,以教学资源的开发及共享为辅助手段,有效地提高教学质量,展开教学改革,提高学生应用智能机器人的实践能力,为社会培养高水平应用型人才。The Intelligent Robot Innovation and Development Practice Course is a highly technical course that enhances students’ understanding and application of robots and artificial intelligence. Through the exploration of this course, it is found that the combination of theory and practice is needed in practical teaching. Based on the latest control theory and Cyber Physical Systems as the theoretical foundation, national competition scenes are introduced as practical methods, and the development and sharing of teaching resources are used as auxiliary means to effectively improve teaching quality, carry out teaching reform, enhance students’ practical ability to apply intelligent robots, and cultivate high-level applied talents for society.
近年来,通过整合外部知识库来提高大语言模型(LLM)的性能,检索增强生成(RAG)取得了显著的成功。通过引用外部知识库,RAG可以完善LLM输出,从而有效解决幻觉、缺乏领域特定知识和过时信息等问题。然而,数据库中不同实体之间复杂的关系结构带来了挑战。对此,GraphRAG利用实体之间的结构化信息来实现更精确和全面的检索,捕捉关系知识并促进与上下文相关的更准确的生成。本文概述了GraphRAG相关技术和技术原理,研究了GraphRAG的下游任务、应用领域和评估标准,最后探讨了GraphRAG的未来研究方向,对未来的技术发展趋势进行了展望。In recent years, Retrieval-Augmented Generation (RAG) has achieved remarkable success in enhancing the performance of large language models (LLMs) by integrating external knowledge bases. By referencing external knowledge bases, RAG can refine the outputs of LLMs, effectively addressing issues such as hallucinations, lack of domain-specific knowledge, and outdated information. However, the complex relational structures among different entities in the databases pose challenges. In response, GraphRAG utilizes the structured information between entities to achieve more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate context-related generation. This paper outlines the related technologies and technical principles of GraphRAG, examines its downstream tasks, application domains, and evaluation criteria, and finally explores future research directions for GraphRAG, offering insights into the future trends of technological development.
文本生成(Text Generation)是自然语言处理(NLP)领域的一项核心技术。由于自然语言自身的复杂性,在内容创作、人机对话、机器翻译等领域的实际应用需求驱动下,文本生成技术长期以来一直是NLP研究的重点、难点和热点。随着深度学习、预训练语言模型等技术的产生和发展,文本生成技术得到长足发展,而基于Transformer的大语言模型(LLM)的产生,则彻底使文本生成技术取得革命性突破。本文旨在对文本生成的技术、模型、范式等方面的历史和现状进行总结,特别侧重于大语言模型对文本生成在框架模型、技术方案、评估基准等方面所带来的变革,以及大语言模型在文本生成领域的典型应用场景,并对文本生成在大语言模型背景下的技术发展趋势进行展望。Text generation is a fundamental technology in the field of Natural Language Processing (NLP). Due to the intrinsic complexity of natural language and the practical demands in applications such as content creation, human-computer interaction, and machine translation, text generation has long been a focal point of NLP research, characterized by its challenges and significant research interest. With the development of deep learning and pre-trained language models, text generation technology has made considerable advancements. The emergence of large language model (LLM) based on the Transformer architecture has brought about a paradigm shift, leading to groundbreaking progress in the field. This paper seeks to provide a comprehensive review of the evolution and current state of text generation techniques, models, and paradigms, with a particular emphasis on the transformative impact of LLM on the design frameworks, technical approaches, and evaluation benchmarks in text generation. Furthermore, this paper explores the representative application scenarios of LLM in text generation and discusses future research directions and technological trends in this domain within the context of LLM.
在人工智能快速发展的时代,各行业对软件工程能力的需求进一步提升,团队协作与现代化工具的使用成为关键。高校软件工程教育亟须与时俱进,引入真实企业项目场景,培养学生的工程意识和团队协作能力。本研究以北京信息科技大学计算机学院的《软件项目综合实践》课程为案例,结合陶哲轩教授对人工智能(AI)与协作模式的理论观点,探索如何通过版本控制工具和AI技术优化教学效果。课程引入Git、Jenkins、Gerrit等工具,结合自动化评分和代码冲突管理,激发学生的学习兴趣,并提升其就业竞争力。研究表明,通过真实企业场景和现代技术的融合,课程改革显著增强了学生的学习动力和就业竞争力,为软件工程教育提供了创新思路。In the era of rapid advancements in artificial intelligence, the demand for software engineering skills across industries has significantly increased, with team collaboration and the use of modern tools becoming critical. Higher education in software engineering urgently needs to keep pace with these developments by incorporating real-world enterprise project scenarios to cultivate students’ engineering mindset and teamwork abilities. This study takes the "Comprehensive Software Project Practice" course offered by the School of Computer Science at Beijing Information Science and Technology University as a case. Combining with Professor Terence Tao’s theoretical viewpoints on artificial intelligence (AI) and collaboration models, it explores how to optimize teaching effectiveness through version control tools and AI technologies. The course introduces tools such as Git, Jenkins, and Gerrit, combined with automated grading and code conflict management, to spark students’ interest and enhance their employability. The findings reveal that integrating real-world enterprise scenarios with modern technologies significantly boosts students’ learning motivation and job market competitiveness, offering innovative approaches to software engineering education.
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