beder’s Post

🔍 Dive into the capabilities of Large Language Models in detecting variability in requirements with Alessandro Fantechi, Stefania Gnesi, and Laura Semini. Get a sneak peek into their research! #LLMs #ResearchPreview #REFSQ24

I am excited to share the launch of our latest blog post featuring LLMpeg, an innovative tool designed for enhanced media processing using large language models. This groundbreaking project not only showcases the potential of LLMs in transforming media applications but also invites the community to engage through comments on its functionality and use cases. Discover the details of LLMpeg and join the conversation. Read more about it here: [LLMpeg on GitHub](https://ift.tt/6vYZKFh).

🌐 Excited to share a comprehensive survey on the spectrum of data contamination in language models! This insightful post delves into the impacts of contamination, detection methods, and mitigation strategies while highlighting areas for further research. A must-read for those interested in the evolving landscape of large language models. Dive into the details here: https://bit.ly/3zgf8Ue #DataContamination #LanguageModels #ResearchSurvey

NEXT GENERATION OF RAG Large Language Models often struggle with hallucinations due to their reliance on parametric knowledge alone.Corrective Retrieval Augmented Generation (CRAG), the next generation of RAG designed to enhance the robustness and accuracy of response generation. CRAG assesses document quality, uses dynamic retrieval strategies, and incorporates large-scale web searches with a decompose-then-recompose algorithm to ensure access to the most relevant information. Just as a child takes the foundation laid by their parent and strives to improve upon it, CRAG advances RAG’s principles for greater accuracy and reliability. Stay tuned for more details on how CRAG is transforming response generation! #GenAI #FutureTech #CRAG #NEXTGeneration #RAG

Retrieval Augmented Generation (RAG) has transformed how we integrate external knowledge with language models, but challenges like retrieval latency and errors persist. Cache Augmented Generation (CAG) offers a solution by preloading documents and precomputing key-value caches for seamless, retrieval-free answers. Our latest blog delves into the workings of CAG, its benefits over RAG, and experimental results showcasing its efficiency in knowledge-intensive tasks. Learn when and how to adopt this innovative approach. To read more check out the link in the comments 👇 #rag #cag

Hey LinkedIn👋 I am excited to share my new project called Verge-Digest! An AI-powered tech news summarizer of The Verge using Google Gemini🪄 🔗Check it out - https://lnkd.in/gQ9jAfH6 🔗GitHub - https://lnkd.in/gr-S-6aN Verge-Digest is your go-to for snappy tech news summaries🗞️from The Verge, straight from the top headlines endpoint of NewsAPI[dot]org. Using Beautiful Soup, it extracts text from those articles & with a click of a button, it crafts its concise summary using Google Gemini 1.5 Pro🔮 Alongside the summaries, it gives you the link to the full article straight from The Verge. And yes, it's all real-time & hosted on Streamlit☁️ #artificialintelligence #machinelearning #datascience #breakintodata #AI #tech #innovation #NLP #llms #GenAI #streamlit

🚀 Transforming RAG Systems with Semantic Chunking Check out Jettro Coenradie's latest blog post on using Large Language Models for semantic text chunking in Retrieval-Augmented Generation systems. With this approach, the accuracy and relevance of data retrieval improves by keeping each text chunk contextually intact. Read the blog here: https://lnkd.in/erCMr7Si

🌟 Exciting news! Check out the latest blog post on "Preble: Efficient Distributed Prompt Scheduling for LLM Serving" (arXiv:2407.00023v1). The post delves into the evolution of prompts for large language models (LLMs) and highlights the need for efficient prompt sharing in LLM serving systems. The paper introduces Preble, a pioneering distributed LLM serving platform that optimizes prompt sharing, showcasing remarkable performance improvements. Dive into the details at https://bit.ly/3VL6ogx to learn more. #SocialMediaMarketing #ProfessionalUpdate

🚀 Introducing 🔥LightRAG🔥 A Simple and Fast Retrieval-Augmented Generation (RAG) System that significantly reduces the costs associated with Large Language Models (LLMs)! 🌐 📄 Read the Paper: https://lnkd.in/gE9m8BKt 💻 Access the Model & Source Code: https://lnkd.in/gqkjuzQ6 Key Features: 🔍 Comprehensive Information Retrieval with Complex Interdependencies LightRAG effectively captures and represents intricate relationships among entities using graph structures. ⚙️ Efficient Information Retrieval through a Dual-Level Retrieval Paradigm LightRAG seamlessly integrates both low-level and high-level information for a thorough and cost-effective retrieval process. ⚡ Rapid Adaptability to Dynamic Data Changes Stay ahead with LightRAG, which quickly incorporates new information as it becomes available.

OpenAI thinks about adding support for DSPy and LLM-gradients. What makes those techniques so powerful? 🧵 LLM-gradients use natural language feedback as "gradients" for prompt optimization, working with any LLM API without needing model internals. This way, the LLM can use its own language understanding for improvements. Developers benefit from faster prototyping, improved reliability in multi-step reasoning, and no task-specific hyperparameter tuning. The optimization process produces interpretable, human-readable edits, which helps with debugging and further improving models. To learn what makes DSPy special: https://lnkd.in/gfcv8aSS