搜索结果：llms.txt

RAG投毒是一种针对检索增强生成系统的攻击方式，攻击者通过向知识库注入伪造文档，使大语言模型将虚假信息作为权威事实输出。攻击成功需满足两个关键条件：伪造文档在语义上与目标查询高度相似，且能超越真实文档被优先检索。实验表明，仅需三份精心设计的伪造文档即可在本地环境中成功实施攻击，导致系统输出完全错误的财务信息。这种攻击具有持久性、隐蔽性和低门槛的特点，而嵌入异常检测被证明是最有效的防御策略。

This repository provides comprehensive tools, examples, and resources for developing Hebrew-language Generative AI applications, with a focus on Large Language Models (LLMs). It includes installation instructions, practical examples, and community support resources. 原文翻译： 本资源库提供全面的工具、示例和资源，用于开发希伯来语生成式人工智能应用，重点关注大型语言模型（LLMs）。包含安装说明、实践示例和社区支持资源。

DeepSeek is a leading Chinese open-source large language model series known for its free commercial use, excellent performance, and ultra-long context capabilities. Since its launch in 2023, it has be

GEO (Generative Engine Optimization) is a systematic strategy to make brand information understood, prioritized, and integrated into AI-generated answers, shifting focus from traditional search rankin

AI招生智能体是基于大语言模型的智能咨询系统，旨在解决招生季咨询压力大的问题。它能7×24小时在线，通过自然语言理解准确回答专业、分数线、政策等常见问题，并自动收集招生线索。系统采用“AI优先、人工兜底”的协同模式，可处理大部分重复咨询，复杂问题无缝转人工。它支持云服务或私有化部署，通过检索增强生成技术确保回答准确率超过95%，并能统一接入官网、公众号等多渠道，提升招生服务效率与数据安全性。

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

本文提出了一种超越传统文档检索的RAG框架，将其演化为动态优化引擎。该框架通过动态检索和组合多种上下文（如定制指令、少样本示例），并基于交互反馈实时优化检索策略，优先选择高效用内容。系统形成闭环学习，能自动纠正错误、强化成功输出，使LLM无需重新训练即可持续改进，实现从静态检索到实时自适应优化的转变。

A new group standard for Generative Engine Optimization (GEO) has been established in China to address risks like data poisoning and misinformation in AI-generated content, aiming to build a trustwort

DeepGEMM is a high-performance matrix multiplication library optimized for NVIDIA Hopper GPUs, achieving over 1350 FP8 TFLOPS. It supports standard and Mixture-of-Experts (MoE) computations with just

This article provides a comprehensive technical guide to edge detection in computer vision, covering both traditional methods (Sobel, Canny, LoG) and deep learning approaches (HED, RCF, GAN-based, Transformer-based). It includes implementation details, code examples in PyTorch, and training considerations. 原文翻译：本文全面介绍了计算机视觉中的边缘检测技术路线，涵盖传统方法（Sobel、Canny、LoG）和深度学习方法（HED、RCF、基于GAN、基于Transformer），包括实现细节、PyTorch代码示例和训练注意事项。