Excited to share our new survey on Multilingual Prompt Engineering!
Multilingual Prompt Engineering is essential to unlock the full power of large language models across diverse languages. Yet with so many strategies emerging over the last few years, it can be hard to know which…
🚀 New survey from MIT Project NANDA charts the future of AI agent registries—comparing MCP, A2A, Entra ID & Nanda Quilt. NANDA’s AgentFacts model stands out with cryptographic trust, privacy, and federated governance for scalable, zero-trust AI ecosystems.#AIAgentRegistry,…
📚🤖 Agentic RAG Survey
A guide exploring agent-enhanced RAG systems. This repository demonstrates single and multi-agent RAG implementations using LangGraph, with practical examples for building intelligent applications.
Explore the evolution of RAG 🔍
github.com/asinghcsu/Agen…
This GitHub repo is a gold mine for EVERY data scientist!
DS Interactive Python repo has interactive dashboards to learn statistics, ML models, and other DS concepts.
Topics include PCA, bagging & boosting, clustering, neural networks, etc.
Fully open-source and free!
New survey Alert: Multilingual Prompt Engineering in LLMs
Covers 39 prompting techniques across 30 tasks & ~250 languages, with a taxonomy of approaches, SoTA methods & insights on high- vs. low-resource language families. #NLP#MultilingualAI#LLM#AI
New survey Alert: Multilingual Prompt Engineering in LLMs
Covers 39 prompting techniques across 30 tasks & ~250 languages, with a taxonomy of approaches, SoTA methods & insights on high- vs. low-resource language families. #NLP#MultilingualAI#LLM#AI
New survey Alert: Multilingual Prompt Engineering in LLMs
Covers 39 prompting techniques across 30 tasks & ~250 languages, with a taxonomy of approaches, SoTA methods & insights on high- vs. low-resource language families. #NLP#MultilingualAIarxiv.org/abs/2505.11665
New survey Alert: Multilingual Prompt Engineering in LLMs
Covers 39 prompting techniques across 30 tasks & ~250 languages, with a taxonomy of approaches, SoTA methods & insights on high- vs. low-resource language families. #NLP#MultilingualAIarxiv.org/abs/2505.11665
Introducing soarXiv ✈️, the most beautiful way to explore human knowledge
Take any paper's URL and replace arxiv with soarxiv (show in video) to teleport to its place in the universe
I've embedded all 2.8M papers up until April 2025
Try it at: soarxiv dot org
🥇🏆 This is the Most Complete Paper on Agentic RAG I've Read – 𝗔𝗻 𝗔𝗯𝘀𝗼𝗹𝘂𝘁𝗲 𝗭𝗲𝗿𝗼-𝘁𝗼-𝗛𝗲𝗿𝗼 𝗝𝗼𝘂𝗿𝗻𝗲𝘆!
If you've ever felt overwhelmed by the technical complexity of Retrieval-Augmented Generation (RAG) or thought, “Where do I even begin?”, this paper is…
This is precisely what our sustainability paper addresses!
A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges
arxiv.org/abs/2412.04782#LLMs#Sustainability#AI
This is precisely what our sustainability paper addresses!
A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges
arxiv.org/abs/2412.04782#LLMs#Sustainability#AI
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