搜索结果：结构化数据

This paper introduces BlockRank, a method that exploits attention sparsity in LLMs for in-context ranking, reducing complexity from quadratic to linear and enabling efficient retrieval of up to 500 documents within a second. 原文翻译：本文提出BlockRank，利用LLM注意力稀疏性进行上下文排序，将复杂度从二次降至线性，实现秒级检索500个文档。

Awesome LLM Apps provides 100+ ready-to-run AI agent and RAG templates with step-by-step tutorials, covering AI Agents, Multi-agent Teams, MCP Agents, RAG, Voice Agents, and more. It's provider-agnostic, works with Claude, Gemini, OpenAI, xAI, Qwen, and Llama, and includes free tutorials on Unwind AI. 原文翻译： Awesome LLM Apps 提供100多个可直接运行的AI智能体和RAG模板，附带分步教程，涵盖AI智能体、多智能体团队、MCP智能体、RAG、语音智能体等。它支持多种模型提供商，包括Claude、Gemini、OpenAI、xAI、Qwen和Llama，并在Unwind AI上提供免费教程。

GPT-3 demonstrates that scaling language models to 175 billion parameters enables few-shot learning across diverse NLP tasks without task-specific fine-tuning, achieving competitive performance through text-only interaction. 原文翻译： GPT-3通过将语言模型扩展到1750亿参数，实现了跨多种NLP任务的少样本学习，无需任务特定微调，仅通过文本交互即可达到竞争性性能。

This article explores how large language models (LLMs) function as document completion engines and demonstrates how to build practical applications by mapping between user and document domains, using GitHub Copilot's prompt engineering pipeline as a detailed case study. 原文翻译： 本文探讨了大型语言模型（LLM）如何作为文档完成引擎工作，并通过在用户领域和文档领域之间建立映射来构建实际应用，以GitHub Copilot的提示工程流程作为详细案例研究。

BlaBlaBlAI is an open-source chat system that enables multiple LLMs and humans to interact in the same room with full awareness of all participants and complete chat history, featuring cost attribution and flexible chat creation. 原文翻译： BlaBlaBlAI是一个开源聊天系统，允许多个LLM和人类在同一个房间内互动，所有参与者都能完全感知彼此并查看完整的聊天历史，具有成本归因和灵活的聊天创建功能。

This comprehensive 2026 ranking analyzes major LLMs across reasoning, coding, math, agentic, software engineering, and chat benchmarks, providing detailed performance scores and pricing comparisons for technical professionals. 原文翻译： 这份全面的2026年排名分析了主要大语言模型在推理、编码、数学、代理、软件工程和聊天基准测试中的表现，为技术专业人士提供详细的性能得分和定价对比。

EDDI (Enhanced Dialog Driven Interface) is a production-grade, config-driven multi-agent orchestration middleware for conversational AI. It coordinates users, AI agents, and business systems through intelligent routing, persistent memory, and API orchestration — without writing code. 原文翻译： EDDI（增强型对话驱动接口）是一个生产级、配置驱动的多智能体编排中间件，用于对话式AI。它通过智能路由、持久化内存和API编排来协调用户、AI智能体和业务系统——无需编写代码。

Leading AI researchers identify four key innovations—steering, memory, tool integration, and multimodality—that will transform LLM capabilities over the next 6-12 months, enabling more reliable, personalized, and actionable AI applications for both enterprise and consumer use cases. 原文翻译： 顶尖AI研究人员确定了四大关键创新——控制、记忆、工具集成和多模态——这些将在未来6-12个月内改变大语言模型的能力，为企业和消费者应用场景提供更可靠、个性化和可操作的AI解决方案。

NVIDIA H100 Tensor Core GPUs set new records across all eight MLPerf training benchmarks, delivering exceptional performance for generative AI and large language models at both per-accelerator and massive scale configurations. 原文翻译： NVIDIA H100 Tensor Core GPU在MLPerf训练基准测试的所有八项测试中均创下新纪录，在单加速器和大规模配置下均能为生成式AI和大语言模型提供卓越性能。

The OpenAI Agents SDK is a lightweight, provider-agnostic Python framework for building multi-agent workflows with features like sandbox agents, tools, guardrails, and real-time voice support. 原文翻译： OpenAI Agents SDK 是一个轻量级、提供商无关的 Python 框架，用于构建具有沙盒代理、工具、护栏和实时语音支持等功能的多智能体工作流。