Manning Publications Co.’s Post

Just came across a paper that delves into the evolution of Retrieval-Augmented Generation (RAG) in the context of Large Language Models (LLMs). It's a must-read for anyone in data science and AI! 🔍 The Three Paradigms: Naive RAG: Focuses on basic retrieval mechanisms. Great starting point for understanding the fundamentals. Advanced RAG: Dives into complex integration of retrieval and generation. A leap towards more sophisticated AI applications. Modular RAG: The future! It emphasizes customizable modules for tailored AI solutions - a game changer in the field! 📈 Each paradigm marks a significant step in the evolution of AI, pushing the boundaries of what's possible in data-driven decision-making and automated content generation. 🔗 Check out the full paper https://lnkd.in/gKSThHPy and the GitHub repository https://lnkd.in/gqK6ihQd for a deep dive into these paradigms. #DataScience #ArtificialIntelligence #RAG #LLMs #Innovation

Jonathan Johnson-Swagel and all my Chain-of-Thought friends out there... 👉 You've heard of Chain-of-Thought but have you heard of Whiteboard-of-Thought? 🔑 Whiteboard-of-Thought" enables multimodal language models to use images as intermediate steps in thinking, improving performance on tasks that require visual and spatial reasoning. 💠 Researchers from Columbia University have developed a new technique that allows multimodal large language models (MLLMs) like OpenAI's GPT-4o to use visual intermediate steps while thinking. 💠While Chain-of-Thought prompts language models to write out intermediate steps in reasoning, Whiteboard-of-Thought provides MLLMs with a metaphorical "whiteboard" where they can record the results of intermediate thinking steps as images! https://lnkd.in/ek_BFdim

💥 FinTral, A Groundbreaking Multimodal Large Language Model for Financial Analysis A collaborative effort between the University of British Columbia and Invertible AI has resulted in the development of FinTral, 💱 a revolutionary LLM specifically designed for the intricate demands of the financial sector. This groundbreaking model, distinguished by its multimodal approach, has showcased exceptional capabilities in processing textual, numerical, tabular, and visual data, setting new standards in financial document analysis. 🎇 Multimodal Approach: FinTral adopts a comprehensive multimodal approach, encompassing textual, numerical, tabular, and visual data to effectively navigate the complexities of financial documents. 🎖 FinSet Benchmark: The researchers introduce FinSet, a robust benchmark tailored for evaluating financial Large Language Models. This benchmark serves as a comprehensive yardstick for assessing the performance of FinTral and potentially other financial LLMs. 📸 Enhanced Vision and Tool Retrieval Functions: FinTral distinguishes itself by incorporating enhanced vision and tool retrieval functions, surpassing established models like GPT-4 in various financial tasks. 📚 Domain-Specific Pretraining: The model builds on the Mistral-7b base model, undergoing domain-specific pretraining on the extensive FinSet dataset. This dataset, comprised of 20 billion tokens sourced from diverse outlets such as C4, news, and financial filings, ensures FinTral's familiarity with the intricacies of financial language. ✏ Instruction Tuning and AI-Driven Feedback: To refine its responsiveness to financial queries, FinTral undergoes instruction tuning and leverages AI-driven feedback mechanisms. 🎞 Visual Data Processing with CLIP Encoders: FinTral integrates visual data processing through CLIP encoders, expanding its capabilities to interpret and analyze visual information in financial documents. 💡 Numerical Task Tools: The model utilizes specialized tools for numerical tasks, further augmenting its proficiency in handling quantitative aspects of financial analysis. 🚀 Direct Policy Optimization and Retrieval Augmented Generation: FinTral employs advanced techniques such as Direct Policy Optimization and Retrieval Augmented Generation, enhancing its ability to address the intricate complexities of financial analysis with unprecedented accuracy and depth. FinTral stands as a pioneering model in the field of financial language processing, showcasing its prowess through a multimodal approach, domain-specific pretraining, and innovative feedback mechanisms. You can read the paper for a detailed explanation: https://lnkd.in/dKTGC9hc #llm #artificialintelligence #financialdata

Context Length Scaling in Large Language Models (LLMs) However, this encoding lacks the ability to extrapolate to sequence lengths beyond those it was trained on. For instance, a model trained on sequences of 2,000 tokens may struggle to maintain its performance when applied to sequences of 65,000 tokens. This is because the Positional Sinusoidal Encoding does not effectively adapt to these longer sequences, leading to a decline in model performance ----- FEEL FREE TO PING ME ON LINKEDIN TO GET AN 'FRIEND' LINK OF THIS ARTICLE, WHICH HELPS US READ ARTICLES WITHOUT PAID ACCOUNT. ----- #largelanguagemodels #contextscaling #positionalembeddings #transformerarchitecture #contextsize #genlnerativeAI

Understanding Large Language Models (LLMs) Read the Blog: https://bit.ly/46hcUjU #datascientist #datascience #datascienceproject #machinelearningproject #artificialintelligencenow #aiprojects #generativeai #naturallanguageprocessing #computervision #objectdetection #datadriven #machinelearningengineer #dataset #spamdetection #neuralnetworks #deeplearning #largelanguagemodels #machinelearningalgorithms #machinelearningmodel

Interesting overview of large language models

Large Language Models Are Drunk at the Wheel https://lnkd.in/eT6-ZDYT

Vikram Chatterji from Galileo hit the nail on the head—deploying Generative AI applications is about more than the model you're using. The models get a lot of media attention. Great conversion with Craig Wiley from Databricks: Generative AI applications have several components that you need to observe, test, and optimize: prompts / LangChain, Vector DBs, embeddings, and search. There's a great research paper from Yunfan Gao et al that summarizes the complexities in RAG and how it's evolving - including modular components. I recommend that everyone looking to build AI applications read it.

Completed a course on "Finetuning Large Language Models". DeepLearning.AI Lamini #AI #GenerativeAI #PromptEngineering #LLM #DeepLearning #Llama #LearningJourney #MachineLearning #DataScience #ProfessionalDevelopment