Badreddine Chaguer’s Post

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📈 Time Series Are Not That Different for LLMs! By harnessing the power of LLMs for time series modeling, we can leverage key features like vast data and transferability to adapt to new tasks. 🔍 Both time series and language data are sequential, allowing similar processing methods like tokenization and embedding lookups. 🛠️ Adapting LLMs for time series involves tokenization, base model selection, prompt engineering, and training paradigm design. 💡 The LTSM-bundle research offers an open-source benchmark framework to evaluate different design choices. Key insights include the effectiveness of simple statistical prompts and tokenization methods with learnable linear layers in training LTSM models. #AI #TimeSeries #LLMs #MachineLearning #DataScience #TechInnovation

🚀 Research Paper Highlights: Let us discuss Multi–layered Thoughts enhanced Retrieval Augmented Generation framework. Similarity is Not All You Need: Endowing Retrieval-Augmented Generation with Multi–layered Thoughts by Chunjing Gan et al from the Ant group. 🚀 Introducing METRAG: A Revolutionary Framework Enhancing Retrieval and Summarization 🚀 🔍 Combination of Similarity and Utility Models: METRAG integrates similarity-based and utility-oriented retrieval models to optimize document retrieval. This ensures documents are not only relevant but also valuable in improving end-task performance, supervised by a large language model (LLM). 📝 Task-Adaptive Summarizer: Featuring a novel task-adaptive summarizer, METRAG extracts the most relevant information from retrieved documents, reducing input size and focusing on essential details. This minimizes information overload and enhances LLM performance by providing concise, relevant context. 🎯 Selective Retrieval Mechanism: METRAG's selective retrieval, enabled by training the utility model with an empty string, allows the model to decide when no external document is needed, leveraging the LLM's inherent knowledge. This optimizes retrieval efficiency and prevents unnecessary document processing. 🔄 End-Task Feedback Alignment: The summarizer is refined by aligning its output with end-task performance. Using Direct Policy Optimization (DPO) principles, the summarizer is trained to produce summaries that directly enhance LLM performance on specific tasks, ensuring highly relevant and useful outputs. 📊 Experimental Superiority: Extensive experiments on knowledge-intensive datasets (e.g., NQ, TriviaQA, HotpotQA, and PopQA) show METRAG significantly outperforms both retrieval-augmented and non-retrieval-augmented baselines. METRAG demonstrates substantial improvements in Exact Match (EM) and F1 metrics, showcasing its effectiveness and multi-layered approach. Further reading- https://lnkd.in/dJwyka5C 🌟 Stay tuned for more updates on upcoming research and analysis in this rapidly evolving landscape of Generative AI. #metrag #RAG #LLMs #innovation #research

What a productive Sunday! 📚☕️ I just wrapped up the first chapter of 𝘿𝙚𝙨𝙞𝙜𝙣𝙞𝙣𝙜 𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙎𝙮𝙨𝙩𝙚𝙢𝙨 by Chip Huyen, and it’s already full of insights! Chapter 1, titled "𝘖𝘷𝘦𝘳𝘷𝘪𝘦𝘸 𝘰𝘧 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘚𝘺𝘴𝘵𝘦𝘮𝘴", dives deep into key topics such as: - The distinction between 𝗠𝗟 𝗶𝗻 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 vs 𝗠𝗟 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 🤖 - How 𝗠𝗟 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 differ for 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 vs 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿-𝗳𝗮𝗰𝗶𝗻𝗴 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 🏢👥 - When to apply 𝗠𝗟—and when 𝗻𝗼𝘁 to! ⚖️ - Differences in stakeholder involvement, computational priorities, data properties, fairness issues, and the need for interpretability in ML projects. Excited to dive into the rest of the book and see how I can apply these lessons! 💡🚀 #MachineLearning #AI #DataScience #MLSystems #TechLearning #ContinuousLearning

A drunk man looking for something not where he lost it, but where the light is better is called the "Streetlight Effect". Streelight Effect is typically tied to Goodhart's law which has several formulations but the one below is the most widely cited: "Every Measure which becomes a target becomes a bad measure" In AI, a famous example of this is the BLEU metric used in machine translation research. Developed in 2001 at IBM, BLEU is a way to automate the evaluation of machine translation systems, and it was a pivotal factor in the machine translation boom of the 00s. Subsequently, By 2010, it was nearly impossible to get a research paper on machine translation into a journal or conference if it didn’t beat the state-of-the-art BLEU score, no matter how innovative the paper was nor how well it might handle some specific problem that other systems were handling poorly. I feel a similar effect is happening in Generative AI where most advancements are done is to make the model larger to target metrics to sell without fundamentally challenging the architecture using first principles. A more fundamental research by working through the first principles is needed to make GenAI more sustainable. Something like the one on which I have written earlier (Link in the comment). #genai #machinelearning #openai #gemini #google #ai #googleresearch #airesearch

🚀 Excited to share our latest video exploring the cutting-edge world of language-based machine learning (LLM) with Vector DB! 📊 Join us as we uncover the pivotal role of Vector DB in powering LLM applications, providing lightning-fast access to text vectors essential for comprehension and generation. 📚 In this video, we compare Vector DB with other industry databases, showcasing its unmatched performance, flexibility, and advanced features. Discover why Vector DB is the top choice for mastering language models in today's competitive landscape. 💡 #MachineLearning #LanguageModels #VectorDB #DataScience #ArtificialIntelligence #AI #Tech #Innovation

We've all heard of GPT-4, but what makes it so special? Experts are buzzing about signs of "general intelligence." Here's the lowdown: 🟠 Beyond Text Tricks: GPT-4 isn't just a whiz with words. It tackles tasks like math, coding, and even law – things most AI models struggle with. 🟠 Learning on the Fly: It doesn't need specific training for each new challenge. It adapts and learns from scratch, more like a human brain. 🟠 Making Connections: Unlike other AI that work in silos, GPT-4 seems to connect information from different areas, leading to surprising, insightful answers. While not a true superintelligence, GPT-4 pushes the boundaries of AI, showing a glimpse of what the future might hold! Learn more about GPT-4 and its potential for general intelligence here: https://hubs.la/Q02s9sfb0 #GPT4 #GeneralIntelligence #AIIsEvolving

🚀 Excited to kick off a new journey on my blog! Today marks the first entry in a series dedicated to unraveling the complexities of probabilistic dictionary learning and its application to Transformer activations. We'll explore how we can extract features from the superposition, pushing the boundaries of our understanding and capabilities. 🔍 In our first post, we lay the groundwork with essential background information and delve into the theory behind dictionary learning and feature extraction. With practical replication work on Toy Models, setting the stage for more advanced explorations in future posts. 💡 Whether you're intrigued by the science, concerned about safety, or passionate about advancing capabilities, this series promises insights and discussions that will spark curiosity and fuel innovation. 👉 Stay tuned and join me in this explorative series as we uncover the secrets of Large Language Models, one post at a time. Don't miss out on the journey – read the first post now and be part of the conversation that shapes the future of Machine Learning. 🔗 [Read the full post here](https://lnkd.in/diDqqWdb) #MachineLearning #LanguageModels #AI #DataScience #ProbabilisticDictionaryLearning #Transformers #MechanisticInterpretability

Excited to delve into "Building LLMs for Production" by Louis-François Bouchard and Louie Peters! This comprehensive guide is an invaluable resource for anyone aiming to master the art of building scalable and reliable AI applications. Covering topics like fine-tuning, Retrieval Augmented Generation (RAG), and deployment, the book is packed with practical tips, and hands-on projects to accelerate the learning journey. Perfect for developers and AI enthusiasts looking to deepen their expertise in both LLM theory and real-world applications. #AI #LLM #MachineLearning #AIApplications

🧠 Unlocking the Power of Human Algorithms: Binary Search in Real Life! 🧠 Ever thought about how our brain naturally tackles complex problems? It breaks them down, adapts, learns, and finds solutions—just like an efficient algorithm! One key concept we can learn from is Pattern Recognition: our brains are wired to spot patterns and make decisions based on them, a crucial skill for coding efficient algorithms. Take a cue from the iconic 3 Idiots scene where Farhan and Raju search for their names on the merit list. Instead of scanning the list from top to bottom, imagine using binary search: 1️⃣ Start in the Middle: Jump straight to the center of the list. 2️⃣ Compare: Are your names before or after this point? 3️⃣ Narrow Down: Skip the half where your name can’t be, and repeat from step 1 again in the half that you chose. By halving the list each time, they would zero in on their names quickly—saving time and stress. This is a perfect example of how algorithms inspired by our brain’s natural problem-solving can make life easier. Let’s build solutions that think like us—efficient, intuitive, and smart! 🚀 #AI #MachineLearning #BinarySearch #PatternRecognition #ProblemSolving #3Idiots #BrainInspiredComputing