Atria AI’s Post

Have you ever wondered how large documents are processed by large language models in #rag? Well, it revolves around chunking, a technique that divides the large corpus of documents into manageable parts or chunks so that it can be fed into the context of an #llm for response generation. However, selecting the optimal chunk size is crucial, as improper chunk sizing can lead to incomplete information, fragmented sentences, and a lack of semantic coherence, resulting in incorrect responses or hallucinations by the LLM. Therefore, rather than hardcoding chunk sizes, it's essential to employ strategies that produce meaningful chunks. Determining the best chunking strategy requires experimentation and evaluating them using metrics to assess how well queries align with context or responses, thus providing a quantitative analysis of the chunking strategy's effectiveness. At Antematter, we're diving deep into this case study, experimenting with different chunking strategies, and are eager to share our findings with you soon 🚀 Stay tuned! #rag #llm #ai #generativeai #reasearch #casestudy #explore

Introducing Pi AI Weekly Trends #7! Our weekly feature brings you the latest AI technology to keep you ahead of the game. This week we highlight 🔹 Gemma 2: The latest set of LLMs from Google, Gemma-2 is available in 2 sizes 9B & 27B and boasts a higher performance than most LLMs of its size https://lnkd.in/dF8nHfkN 🔹 Olmo: A state-of-the-art open language model and framework from the Allen AI Institute for building and studying the science of language modelling https://pischool.link/Olmo 🔹 The Prompt Report: An exhaustive survey of prompting techniques covered by a large and diverse group of researchers. This paper includes text-only, multilingual and multimodal prompting strategies https://lnkd.in/d3VGeK2z Our Machine Learning Scientist, Vijayasri Iyer, has selected these links for you. Was this helpful? Let us know by liking and sharing! #MachineLearning #DeepLearning #LLM #PiAIWeeklyTrends

LanguageWire introduces Retrieval Augmented Generation (RAG) to enhance language services In the evolving landscape of the language industry, a powerful new approach is gaining momentum: Retrieval Augmented Generation (RAG). This innovative technique harnesses the capabilities of large language models (LLMs) by using prompts to imbue them with knowledge, data, and context. https://is.gd/MxAoZD #ai #aitechnology #artificialintelligence #languagewire #llm #machinelearning

Just completed an exciting course on the deeplearning ai platform - Large Language Models with Semantic Search. The course delves deeper into the technical aspects of information retrieval through semantic search. During the course, I learned about utilizing Cohere's various language models, including their Multilingual Embedding, ReRank, and Generation models, as well as working with Weaviate as the Vector database. Additionally, I experimented with different chunking approaches using AnnoyIndex to construct the semantic search from the ground up. It was an incredible learning experience, and I cannot wait to apply these newly acquired skills to my work. Thank you Jay Alammar, Luis Serrano, for this interesting course, and Andrew Ng for all short but powerful courses on deeplearning ai platform. #deeplearning #AI #SemanticSearch #InformationRetrieval #Cohere #Weaviate #AnnoyIndex

🔎 Large Language Models (LLM) have reimagined how search is defined. From the conventional archive, index, and retrieve processes, embeddings and generations are the new paradigms. 🔍 'Semantic' notion in search became more semantic and overwrites many processes in building search solutions. 🤟🏻 Many sophisticated technologies, such as Named Entity Recognition, Custom tokenizations, dynamic facets, and clustering, were state-of-the-art processes. 🙋🏻♀️ What are your thoughts on the future of search powered by LLM and how it is going to transform Enterprise Search? 🧐 What two new chapters will you anticipate if 'Introduction to Information Retrieval' is republished? (Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.) #llm #genai #generativeai #largelanguagemodels #ai #aiml #datascience #machinelearning #search #informationretrieval #nlproc

🎥 Delving into Ongoing Research Areas Around Conversational LLMs! 💬 I've released a video covering the latest research in Conversational LLMs (Language Models). 🌐🚀 In this video, we explore key ingredients of 100k+ context windows like Flash Attention, Sparse Attention, Multi-query Attention, and conditional computation. We also discuss techniques such as pruning, distillation, and Quantization, along with the emerging trend of LORA-based fine-tuning for language models. 🌟🔬 We also dive into efficiency-enhancing methods like Retrieval augmented LLMs. Additionally, we touch upon responsible AI research, addressing ethical and social issues associated with LLM outputs. And don't miss the fascinating concept of AUTO-GPT! 🤖💬 Video Link: youtu.be/mF7OM_XU2S4 YT channel Link:  youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

Boost Your Large Language Model's Power with Advanced RAG Algorithms Want to generate even more informative and relevant text with your Large Language Model (LLM)? Advanced Retrieval-Augmented Generation (RAG) algorithms are here to help! These techniques fine-tune the information retrieval process, feeding your LLM with the most relevant data for superior text generation. In this blog, we explore 4 key advanced RAG algorithms: Query Expansion - Sharpen your search for highly relevant information. Self-Query Retrieval - Leverage internal knowledge for a more cohesive output. Hybrid Search - Combine retrieval strategies for ultimate efficiency. Re-Rank - Prioritize the most valuable information for accurate generation. Do you currently use RAG for your LLM? Share your experiences and any questions you have about advanced RAG algorithms in the comments below! #RAG #LLM #AI #MachineLearning #NaturalLanguageProcessing

RAG vs. Long-Context LLMs — Who is winner ?: It’s like choosing between a librarian who fetches the exact book you need and a scholar who reads the entire library to answer your… Continue reading on Generative AI » #genai #generativeai #ai

Explore the mesmerizing world of large language models and machine learning with the latest article, “The Illusory Superpowers of Oversized Linguistic Models: A Tongue-in-Cheek Exploration". Understand how these models function like the human brain, devouring nearly a trillion words from the internet through machine learning, to generate human-like text. But as they learn and adapt, they also stumble upon unexpected lessons…read on to delve into an intriguing exploration of this fascinating AI realm. #AI #MachineLearning #LanguageModels #TechTrends Link: https://lnkd.in/eKXdCG2K

🌐 Key Factors Driving Sophisticated Results in LLMs 🤖 Several factors work together to produce the sophisticated and accurate results we see from Large Language Models (LLMs). Let’s break them down: Text Pre-processing 🧹 Cleaning and organizing raw text data to make it usable for the model. Text Representation 📄 Converting words into numerical formats, enabling the model to understand and process them. Pre-training 📚 Exposing the model to vast amounts of text data to help it learn language patterns and structures. Fine-tuning 🔧 Refining the model with specific tasks and datasets to improve its performance on specialized tasks. Advanced Fine-tuning 🎯 An additional layer of tuning, often applied for complex or domain-specific tasks, further enhance precision and accuracy. Each of these stages plays a critical role in shaping the performance of LLMs, making them the powerful tools they are today. #LanguageModels #AI #MachineLearning #LLMs #DataScience #TechInsights 🚀