论文

本文综述了 Islamic LLMs 与可信 Islamic AI 这一新兴领域，并论证了仅具备阿拉伯语流利度不足以支撑 Islamic AI，进而提出面向抗幻觉 Islamic AI 系统的研究议程。This survey reviews the emerging field of Islamic LLMs and trustworthy Islamic AI, and argues that fluency in Arabic is not sufficient for Islamic AI, with a research agenda for hallucination-resistant Islamic AI systems.

本文对近期 KV cache 优化技术进行系统综述，将其归纳为五大方向：cache eviction、cache compression、混合内存方案、新型 attention 机制与组合策略，并指出自适应多阶段优化流水线是未来研究的重要方向。This paper provides a systematic review of recent KV cache optimization techniques, organizing them into five principal directions: cache eviction, cache compression, hybrid memory solutions, novel attention mechanisms, and combination strategies, and pointing toward adaptive, multi-stage optimization pipelines as a promising direction for future research.

本文对 LLM 推理服务方法进行了全面综述，涵盖基础的实例级方法、深入的集群级策略、新兴的场景方向以及其他重要但零散的领域。This paper provides a comprehensive survey of LLM inference serving methods, covering fundamental instance-level approaches, in-depth cluster-level strategies, emerging scenario directions, and other miscellaneous but important areas.

本文探讨了 cloud platform 与 distributed system 在支撑 LLM 可扩展性、效率与优化方面的作用，涵盖数据管理、资源优化，以及对 microservices、autoscaling 与 hybrid cloud-edge 方案的需求。The role of cloud platforms and distributed systems in supporting the scalability, efficiency, and optimization of LLMs is explored, including data management, resource optimization, and the need for microservices, autoscaling, and hybrid cloud-edge solutions.

本文旨在为研究者与从业者提供一份快速、全面的参考，通过对现有工作的广泛、信息密集型总结来汲取洞见，以推动 LLM 研究的发展。This review article is intended to provide a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.

本文试图以简洁的方式描述受生成式 AI 影响的主要行业与模型，并给出近期主要生成式模型的分类体系。This work consists on an attempt to describe in a concise way the main models are sectors that are affected by generative AI and to provide a taxonomy of the main generative models published recently.

本文综述了一些最具代表性的 LLM，包括三大主流 LLM 家族（GPT、LLaMA、PaLM），讨论其特性、贡献与局限性，并概述了构建与增强 LLM 的相关技术。This paper reviews some of the most prominent LLMs, including three popular LLM families (GPT, LLaMA, PaLM), and discusses their characteristics, contributions and limitations, and gives an overview of techniques developed to build, and augment LLMs.

本文旨在建立一套系统化的开放问题与应用成果清单，以便机器学习研究者更快地理解该领域的现状并开展有效工作。This paper aims to establish a systematic set of open problems and application successes so that ML researchers can comprehend the field's current state more quickly and become productive.

提出一个系统性的、以架构为中心的 LLM 记忆分类法，沿三个正交轴刻画记忆：表示、更新动态与持久性，有效桥接不同的架构范式。A systematic, architecture-centric taxonomy of memory in LLMs is presented, characterizes memory along three orthogonal axes: representation, update dynamics, and persistence, effectively bridging disparate architectural paradigms.

本文讨论了一种被称为大语言模型涌现能力的不可预测现象——若某项能力在小模型中不存在而在大模型中存在，则称为涌现。This paper discusses an unpredictable phenomenon that is referred to as emergent abilities of large language models, an ability to be emergent if it is not present in smaller models but is present in larger models.

本文给出 ICL 的形式化定义，厘清其与相关研究的联系，并梳理讨论训练策略、提示设计策略及相关分析等高级技术。This paper presents a formal definition of ICL and clarify its correlation to related studies, and organizes and discusses advanced techniques, including training strategies, prompt designing strategies, and related analysis.