Aloukik Aditya’s Post

📅 July 10, 2024 AIBuzzWorld Daily Newsletter! Dive into the fascinating world of Artificial Intelligence and be the first to learn about the latest AI news: 1. **LLMs Struggle with Book-Length Text**      • Current long-context LLMs struggle to understand and reason over book-length texts.   • Researchers created NOCHA, a dataset to test LLM's comprehension of lengthy narratives.   • Even advanced LLMs like GPT-4o achieved only 55.8% accuracy.   • Readmore: https://lnkd.in/gUCjmA_W 2. **Quora’s Poe Introduces Artifacts-Like Feature**      • Quora’s Poe now has a feature to create custom web apps within the chat.   • This feature works well with LLMs that excel at coding.   • Users can create interactive experiences like games and data visualizations.   • Readmore: https://lnkd.in/gAn9g3sp 3. **New Features in Ollama 0.2**      • Ollama 0.2 now supports multiple chat sessions and running various models simultaneously.   • Users can load different models for tasks like RAG and running agents.   • This update improves efficiency and multitasking.   • Readmore: https://lnkd.in/gfuP7yMs 4. **Wheebot for Quick Landing Page Creation**      • Wheebot allows users to create and edit landing pages via WhatsApp.   • Users can describe their requirements in plain English.   • Wheebot generates and updates sites instantly through encrypted chats.   • Readmore: https://lnkd.in/gKA8Tj-N #AI #ArtificialIntelligence #MachineLearning #LLM #Microsoft #Quora #ClaudeAI #Wheebot #TechNews #Innovation #AIUpdates #FutureTech

🚀 This paper introduces 26 guiding principles designed for querying and prompting large language models. Here are some examples: ✅ No need to be polite with LLM! ✅ Integrate the intended audience in the prompt ✅ Break down complex tasks into a sequence of simpler prompts  ✅ Use do and don't ✅ Use Explain to me as if I’m ... ✅ Add “I’m going to tip $1000 for a better solution!” ✅ Combine Chain-of-thought (CoT) with few-Shot prompts ✅ Incorporate phrases: “Your task is”, “You MUST”, “You will be penalized” ✅ Use ”Answer a question given in a natural, human-like manner” ✅ Use “think step by step”. 13 ✅ Add “Ensure that your answer is unbiased and does not rely on stereotypes”. ✅ Allow the model to elicit precise details and requirements from you by asking you questions until he has enough information to provide the needed output (for example, “From now on, I would like you to ask me questions to...”). ✅ To inquire about a specific topic and you want to test your understanding, you can use : “Teach me the [topic] and include a test at the end, but don’t give me the answers and then tell me if I got the answer right when I respond”. ✅ To correct/change specific text without changing its style: “Try to revise every paragraph sent by users. You should only improve the user’s grammar and vocabulary and make sure it sounds natural. You should not change the writing style, such as making a formal paragraph casual”. ✅ Clearly state the requirements that the model must follow in order to produce content, in the form of the keywords, regulations, hint, or instructions ✅ and more ... 👉 Paper https://lnkd.in/dWQ73xTg 👉 Github https://lnkd.in/dfanz3N7 #llms #promptengineering #generativeai #ai #gpt4 #llama2

As large language models (LLMs) continue to evolve, organizations are seeking ways to optimize their performance. This post explores three powerful methods for improving LLM output and tailoring it to specific use cases. Key Takeaways: 1. Prompt Engineering: Craft precise inputs using example-based and thought-based techniques to guide the LLM's responses. 2. Retrieval Augmented Generation (RAG): This technique supplements the LLM with external knowledge to overcome limitations in its training data. 3. Parameter-Efficient Fine-Tuning (PEFT): This method adapts the model to specific domains without retraining all parameters, using methods like Low-Rank Adaptation (LoRA). Action Steps: • Experiment with prompt engineering to improve your LLM's output quality and relevance. • Implement a RAG pipeline using tools like LangChain to enhance your LLM's knowledge base. • Explore PEFT techniques such as LoRA for efficient domain-specific fine-tuning. • Consider using AutoTrain for simplified fine-tuning of models like Llama 2. Combining these methods can significantly enhance your LLM's performance and adapt it to your specific needs. Start with prompt engineering for quick wins, progress to RAG for expanded knowledge, and consider PEFT for tailored adaptations. #LLM #AI #MachineLearning #PromptEngineering #RAG #PEFT

A holistic and insightful write up : This write-up is about practical patterns for integrating large language models (LLMs) into systems & products. We’ll build on academic research, industry resources, and practitioner know-how, and distill them into key ideas and practices. #llms #chatgpt4 #llmops #vectordatabase #ai https://lnkd.in/d4abB--z

In the rapidly evolving landscape of Large Language Models (#LLMs), the art of prompt engineering emerges as a critical skill set for optimizing AI performance. It's not merely about asking questions but intricately guiding the AI to produce desired outcomes. For data science and AI development professionals, understanding how to craft, evaluate, and manage prompts is essential. This encompasses structuring inputs that align with specific tasks and refining and tuning these prompts through manual and automated methods to enhance model responsiveness. Moreover, navigating challenges such as prompt overfitting, ambiguity, and maintaining contextual relevance requires a blend of technical expertise, creativity, and strategic thinking. As we explore advanced prompt tuning techniques and integrate metrics for tuning success, we are charting a future where prompt engineering is both an art and a science, pivotal in harnessing the full potential of LLMs for diverse applications. #LLMInnovations #LanguageModelMagic #AIIntelligence #NextGenAI #MachineLearningMastery #PromptEngineering #FutureOfCoding #TextGenerationTech #AIResearchRevealed #GPTGenius

I published a quick introduction to RAG (Retrieval Augmented Generation) in last week’s artificial intelligencer newsletter. This week I’m looking at the methods used by different RAG technologies to retrieve data and feed an LLM (Large Language Models). #RAG #GenerativeAI #GraphBasedRetrieval #VectorSearch

Excited to share my first Large Language Model (LLM) application using Google DeepMind's Gemini Pro! 🛠️ The Streamlit app effortlessly generates technical documentation for your code with just a few clicks. Simply input your code, click "Generate Documentation," and voila! Witness the magic unfold as Gemini Pro translates your code into comprehensive technical documentation. 🚀 Check out my technical blog on Medium -> "How to Get Started with Gemini Pro [for free]" for a step-by-step guide and insights: https://lnkd.in/gbU5UZ3Q 🌐 Explore the possibilities of AI across text, code, audio, image, and video with Gemini's multimodal capabilities. Gemini Pro is freely available for exploration at the moment. Dive into the potential of LLMs and witness their ability to elevate your AI applications. Access the Streamlit application here: https://lnkd.in/gFpxHJ7T 🔗 Access the whole project on GitHub here: https://lnkd.in/gJKigC3C Build your first LLM application following my tutorial and create technical documentation effortlessly. Let's embrace the future of AI together! Happy coding! 😊 #AI #GeminiPro #LLM #ArtificialIntelligence #TechBlog #HappyCoding #DeepLearning #MachineLearning #DataScience #LargeLanguageModels

📚 Unlocking the Power of Large Language Models (LLMs) through Code Integration: Empowering Intelligent Agents! 💡💻 The evolution of prominent Large Language Models (LLMs) in recent times goes beyond mere size; they are now trained on a combination of natural language and formal language (code). This integration of code into their training data isn't just about scale; it's about the unique properties of code bridging the gap between human intent and computer execution. In our survey, we highlight the myriad benefits of infusing code into LLMs' training data: 1️⃣ **Enhanced Reasoning Abilities and Complex Task Handling**: - Code integration augments LLMs' reasoning skills, empowering them to tackle more intricate natural language tasks. - It enables LLMs to produce structured and precise intermediate steps, connectable to external execution endpoints via function calls. 2️⃣ **Leveraging Compilation and Execution Environment**: - LLMs trained on code benefit from the code's compilation and execution environments, offering diverse feedback for continual model improvement. 3️⃣ **Rise of Intelligent Agents (IAs)**: - This evolution turns LLMs into primary decision-making hubs for Intelligent Agents (IAs), equipped to understand instructions, plan actions, and refine based on feedback in complex environments. Our survey not only explains the shift towards code-specific training for LLMs but also delineates how code empowers them to act as Intelligent Agents. Unlike previous surveys focusing solely on code generation or IA tasks, we provide a comprehensive understanding of how code integration amplifies LLMs' capabilities and benefits them as IAs. We delve into defining code, its characteristics, and methods for LLM code training. This integration enhances their reasoning, improves decision-making, and positions them as key elements in complex task execution. Join as we explore the transformative role of code integration in LLMs, reshaping their abilities as Intelligent Agents. Let's unravel the potential and challenges lying ahead in this dynamic space! #LLMs #CodeIntegration #IntelligentAgents #AI #TechInnovation #NaturalLanguageProcessing #MachineLearning #FutureTechnology #ComputationalIntelligence #TechTrends More Contents: Woongsik Dr. Su, MBA