This survey paper outlines the key developments in the field of Large Language Models (LLMs), including enhancements to their reasoning skills, adaptability to various tasks, increased computational efficiency, and the ability to make ethical decisions. The techniques that have been most effective in bridging the gap between human and machine communications include the Chain-of-Thought prompting, Instruction Tuning, and Reinforcement Learning from Human Feedback. The improvements in multimodal learning and few-shot or zero-shot techniques have further empowered LLMs to handle complex jobs with minor input. A significant focus is placed on efficiency, detailing scaling strategies, optimization techniques, and the influential Mixture-of-Experts (MoE) architecture, which strategically routes inputs to specialized subnetworks to boost predictive accuracy, while optimizing resource allocation. This survey also offers a broader perspective on recent advancements in LLMs, going beyond isolated aspects such as model architecture or ethical concerns. Additionally, it explores the role of LLMs in Agentic AI and their use as Autonomous Decision-Making Systems, and categorizes emerging methods that enhance LLM reasoning, efficiency, and ethical alignment. The survey also identifies underexplored areas such as interpretability, cross-modal integration, and sustainability. While significant advancements have been made in LLMs, challenges such as high computational costs, biases, and ethical risks remain. Overcoming these requires a focus on bias mitigation, transparent decision-making, and explicit ethical guidelines. Future research will generally focus on enhancing the model's ability to handle multiple inputs, thereby making it more intelligent, safe, and reliable.

翻译：本综述论文概述了大型语言模型（LLMs）领域的关键进展，包括其推理能力的增强、对多样化任务的适应性、计算效率的提升以及伦理决策能力的构建。在弥合人机沟通差距方面最为有效的技术包括思维链提示、指令微调以及基于人类反馈的强化学习。多模态学习与小样本/零样本技术的进步进一步赋能LLMs以少量输入处理复杂任务。研究特别聚焦于效率问题，详细阐述了扩展策略、优化技术以及具有影响力的混合专家（MoE）架构——该架构通过将输入智能路由至专用子网络以提升预测精度，同时优化资源分配。本综述还提供了关于LLMs最新进展的更广阔视角，超越了模型架构或伦理问题等孤立层面。此外，文章探讨了LLMs在智能体人工智能中的作用及其作为自主决策系统的应用，并对提升LLM推理能力、效率及伦理对齐的新兴方法进行了分类。同时指出了尚未充分探索的领域，如可解释性、跨模态整合与可持续性。尽管LLMs已取得显著进展，但高计算成本、偏见及伦理风险等挑战依然存在。克服这些挑战需要聚焦于偏见缓解、透明化决策与明确的伦理准则。未来研究总体上将致力于增强模型处理多模态输入的能力，从而使其更具智能性、安全性与可靠性。