Rajesh Srivastava

A Survey of RAG and RAU: Advancing Natural Language Processing with Retrieval-Augmented Language Models Quick read: https://lnkd.in/gnGqk44W Paper: https://lnkd.in/g8-VftbX #llms #largelanguagemodels #ai

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My latest Medium post comparing SetFit, FastFit, and Semantic Router for Few-shot NLP Intent Classification has been published by Towards AI! This post covers: 1. Details of NLP Intent classification and why it is extremely important in the age of LLM chatbots 2. Algorithm details of SetFit, FastFit and Semantic Router 3. Detailed code experiments on a chatbot intent detection dataset 4. Conclusion on the best method as of today. As with most of my posts, it has lots of code you can directly use in your work! This post was jointly written by Shilpa and me, both of us humans 😅 🔗 Medium Friend Link (without paywall): https://lnkd.in/dVfAvjzr Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

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I have been searching for the perfect tool that will do research and write a 3-5 pager report on any topic. Till now I have been very impressed by GPT Researcher https://lnkd.in/gBwJkjEp But now there is a new contender https://lnkd.in/gJHFCNrD Both these tools use a chain of Agents. Each task spawns multiple agents and once each agent is done, the tool summarizes the report from all the agents. (An Agent is a LLM doing a specialized task.). The Chain of Agents shows great promise beyond reports and can be used in multiple industries like Insurance, Finance, Stock trading etc. Tell me what you think ?

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⌛ Don't miss Agriya Khetarpal's session on Python in WebAssembly via Pyodide at #PyConIndia2024.📊 Discover how the PyData ecosystem is evolving for the web! ➡️ https://in.pycon.org/2024/ #SciPy #OpenSource

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Revised Evaluation of LLM Compression Using Distance Metrics Researchers from Microsoft Research, India, propose an advanced evaluation framework incorporating distance metrics like KL-Divergence and percentage flips alongside traditional accuracy metrics. https://is.gd/MDzsSN #ai #aitechnology #artificialintelligence #llm #llmcompression #machinelearning

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🤔 Interesting perspective from Infosys Chairman Nandan Nilekani: "Let Silicon Valley build LLMs; India should lead in AI use cases." At Meta's Build with AI summit, Nilekani suggested that India should focus on practical AI applications rather than competing in the LLM space. But is this the right approach? Key points to consider: • Silicon Valley giants are heavily invested in LLM development • India could focus on: - Synthetic data generation - Small language models - Real-world applications - Building robust data infrastructure The vision is for India to become the global "use case capital" for AI, emphasizing frugal innovation and practical solutions. What do you think? 💭 Should India stick to developing practical AI applications? 💭 Or should we also invest in fundamental AI research and LLM development? 💭 Could focusing solely on use cases limit our AI capabilities in the long run? Let's discuss! Share your thoughts in the comments below. 👇 #AI #DigitalIndia #LLM

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How are Graph Convolutional Neural Networks (GCNNs) a generalisation of Convolutional Neural Networks (CNNs)? My next blog in the Graph series on "Deep Learning with Graphs" is on the way (well, it's already written on paper) and it's going to be a blockbuster. The beauty of Graph Machine Learning is that the more you read about it, the more you feel that your neurons (real ones!) are learning to think better! And you're already in love with it! Coming to the point: One might recall the convolution operations in a CNN. You have a grid of pixels representing an image. You have a 3 x 3 filter. You slide the filter over the image and carry out aggregation to obtain a new pixel value. You proceed in the same fashion over the whole grid, sliding towards the right , bottom, carrying out the convolution operation and finally getting a new transformed, reduced image through the series of aggregations. You repeat this through the different convolutional layers which together form a Convolutional Neural Network Now think of a Graph. It is not simple grid like an image. The topology of a Graph is arbitrary. There is no notion of left, bottom, right in a Graph like in an image which is represented through a grid of pixels. You cannot slide over a Graph as simplistically you did and performed the convolutional operations in an image. How do you approach the problem? How do you carry out convolutional operations in a Graph? Now take a node in the Graph. Take all the nodes which are the immediate neighbours of this node. The convolutional operation here is: You aggregate the information from all the neighbours and pass it to the node. The information is nothing but the emedfings of the neighbour nodes. You add together the emedding of the node under consideration. You transform the aggregated embedding by multiplying with a weight Matrix and transform the node's own embedding by multiplying with a different weight matrix. This is the typical linear transformation operation in a NN. You apply non linearity - the activation function like ReLu. Now think about a CNN taking the same grid and considering a central pixel. Consider the rest of the pixels around the central pixel. You are essentially doing the same thing: taking information from the neighbour pixels along with the central pixel, aggregating, as I described above for the node in the Graph. Hence a Graph Convolutional Network is a generalisation of CNN The story goes, you do the same for every node on the Graph. Each node in the Graph represented through a different neural network architecture! This is amazing 🔥🔥🔥💓💓💓 #graphs #machinelearning #deeplearning My Technical Blogs Library: 1) https://lnkd.in/ejyWacXn 2) https://lnkd.in/bfrkRjq Follow Ajay Taneja for insights on: Data Science, Data Engineering, Finite Element Analysis, Non Linear Finite Element Analysis, Fatigue, Fracture Mechanics

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What is regularization in machine learning? Why would you use weight regularization, and how should you interpret its different types? Dhruv Matani and Naresh Singh share a clear explainer that addresses these (and a few more!) questions.

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Quantized embeddings offer significant advantages for NLP models, including reduced storage requirements, faster computations, lower energy consumption, and improved model interpretability. They are becoming increasingly important for enabling efficient and scalable NLP on resource-constrained devices and cloud-based applications. Read my latest article below

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A big shoutout to Kunal Raj for providing me with a sample PDF version of the "Mastering Transformers" book for review. It looks promising, covering many important topics from BERT to Stable Diffusion. I'll share my full review once I finish reading it! You can buy it here - https://packt.link/zfTHS

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Find Sajjadur Rahman (hiring) at the #GuideAI workshop a #SIGMOD2024, presenting #CMDBench, a benchmark designed to model the complex landscape of enterprise data platforms. By adapting existing datasets, CMDBench evaluates coarse- and fine-grained data discovery and task execution. Join Sajjadur for his presentation at 3 PM CST during Session 3. https://lnkd.in/g3ETpFME #MachineLearning #AI #LLMs #Data #DataScience #AI

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Text chunks in retrieval may risk losing context. The below article introduces contextualizing each chunk using an LLM. The added cost and latency required for contextualizing is partially addressed through a lightweight LLM (haiku) and prompt caching. Along with other known techniques (BM25, reranking, etc.), a significant reduction in retrieval failure is achieved. Overall, a good article to read. https://lnkd.in/g_5fgAN8

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Delving into Details:  𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐯𝐬. 𝐅𝐥𝐚𝐬𝐤 for 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠  Applications. Which framework aligns best with your ML projects? 🤔 Let's explore the variances! 💡 Also, follow us on: Facebook: http://bit.ly/41wCgrz Instagram: https://bit.ly/3NL6RMz Twitter: https://bit.ly/3tkjyqK Threads: https://bit.ly/3NNUgYS #FastAPI #Flask #MachineLearning #Python #FrameworkComparison #Regbits

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