搜索结果：人工智能

Retrieval-Augmented Generation (RAG) enhances large language models by allowing them to access external knowledge bases before generating responses, improving accuracy, relevance, and cost-effectiveness without retraining. 原文翻译： 检索增强生成（RAG）通过让大语言模型在生成响应前访问外部知识库来优化其输出，无需重新训练即可提高准确性、相关性和成本效益。

GEO (Generative Engine Optimization) is emerging as a key AI marketing strategy, focusing on optimizing brand knowledge assets and content structure to enhance visibility in AI-driven user interactions and search behaviors. 原文翻译： GEO（生成式引擎优化）正成为关键的AI营销策略，专注于优化品牌知识资产和内容结构，以提升在AI驱动的用户互动和搜索行为中的可见性。

DeepSeek V4 预览版于2026年4月发布，提供Pro（1.6T参数）和Flash（284B参数）两个版本，均支持1M tokens上下文和领先的Agent能力。其核心Agent能力包括Function Calling（支持并行/嵌套工具调用）、长上下文记忆、思考模式（推理增强）、结构化输出、多模态理解及主流Agent框架适配。API兼容OpenAI接口，支持流式/非流式调用。典型应用覆盖

This paper introduces Generative Engine Optimization (GEO), a black-box optimization framework to help content creators improve their visibility in generative engine responses. It proposes GEO-bench f

WeChat has begun testing an AI-powered search feature integrating the DeepSeek-R1 model, offering a more diverse and intelligent search experience. The feature is currently in limited testing, pulling

This paper provides a comprehensive introduction to knowledge graphs, covering data models, query languages, knowledge representation techniques, creation methods, and applications across both open and enterprise contexts. 原文翻译： 本文全面介绍了知识图谱，涵盖数据模型、查询语言、知识表示技术、创建方法以及在开放和企业环境中的应用。

This article provides a comprehensive guide on DeepSeek SEO and AI GEO optimization, covering 16 steps for AI reasoning optimization, 15 steps for GEO optimization, prompt types, and keyword strategie

This article provides a comprehensive 2026 guide for Chinese users to access and utilize Google's Gemini models, particularly Gemini 3 Pro, overcoming regional access barriers through platforms like n.myliang.cn. It covers practical applications in multimodal tasks, AI-assisted office work, programming, and SEO optimization. 原文翻译： 本文为国内用户提供了一份全面的2026年指南，介绍如何通过n.myliang.cn等平台访问和使用谷歌Gemini模型（特别是Gemini 3 Pro），以克服地域访问限制。内容涵盖多模态任务、AI辅助办公、编程和SEO优化等实际应用。

This article analyzes the Rejection sampling Fine-Tuning (RFT) method for enhancing large language models' mathematical reasoning. It details a process where smaller models generate diverse reasoning paths, which are filtered for quality and diversity, then used to fine-tune a larger model (e.g., Llama2-70B). Key findings show RFT significantly improves accuracy over standard Supervised Fine-Tuning (SFT), especially for weaker models, by increasing the variety of unique reasoning paths in the training data. 原文翻译： 本文分析了用于增强大语言模型数学推理能力的拒绝采样微调（RFT）方法。它详细阐述了一个流程：使用小模型生成多样化的推理路径，经过质量和多样性筛选后，用于微调更大的模型（如Llama2-70B）。核心发现表明，通过增加训练数据中独特推理路径的多样性，RFT相比标准监督微调（SFT）能显著提升模型准确率，对于性能较弱的模型提升尤为明显。