Semantic Web Company (SWC)’s Post

Augmenting LLMs with Knowledge Graphs — From WMG to RAG Knowledge graphs offer a structured representation of data, including entities, their attributes, and the relationships between them. These graphs are constructed from source corpora and represent a higher-level abstraction of semantic concepts and dependencies. #retrievalaugmentedgeneration #rags #knowledgegraphs #ontologies #largelanguagemodels #ai #aiapplications

LOMO is a memory-efficient optimization technique that outperforms traditional methods like Adam and SGD, especially in fine-tuning scenarios. Unlike Adam, which requires significant memory to store various optimizer states, and SGD, which stores gradients for the entire network requiring large memory, LOMO introduces a more memory-efficient approach by updating the network layer by layer. This method only necessitates storing gradients for one layer at a time. To counteract the issue of exploding or vanishing gradients, LOMO employs a unique strategy involving two backward passes: the first pass calculates the gradient magnitude across the whole network, and the second pass adjusts each layer's gradients based on this magnitude before updating parameters. This approach not only reduces memory usage but also enhances performance compared to techniques like LoRA, which only fine-tune a small subset of parameters, limiting performance improvements. LOMO, by fine-tuning all parameters, achieves optimal performance gains with lower memory demands. Link - https://lnkd.in/giCx-NkR

Qwen2-VL: Hands-On Guides for Invoice Data Extraction, Video Chatting, and Mutlimodal RAG with PDFs: How to use a top, open-source, vision language model Continue reading on Generative AI » #genai #generativeai #ai

Retrieval Augmented Generation is a cornerstone technology that allows LLMs to work with large sets of documents. https://buff.ly/4bCkdEp author: https://buff.ly/3JVGHEn

Optimizing LLMs: Insights on 32-bit, 8-bit, and Paged AdamW Techniques - https://lnkd.in/ejMQBqxp - #32bit #8bit #AdamW #Insights #LLMs #Optimizing #Paged #Techniques

I created a system that lets me use o1-preview to rate the human-level quality of results from lesser models. I give it: * The input * The prompt * The output And then I use o1 to assess how well it did. And it's flexible so you can use whatever models, upgrade the rating system, or whatever. https://lnkd.in/gZ5RZPS4

NavGPT-2: Integrating LLMs and Navigation Policy Networks for Smarter Agents LLMs excel in processing textual data, while VLN primarily involves visual information. Effectively combining these modalities requires sophisticated techniques to align and correlate visual and textual representations. Despite significant advanceme... https://lnkd.in/eEGR4_8r #AI #ML #Automation

Large Language Models (LLMs) are revolutionizing modern life by excelling in language processing, code generation, and knowledge synthesis. AI for Good features Maria Antonia Brovelli and Hamid Mehmood, highlighting how LLMs are utilized for mapping and monitoring using Earth Observation (EO) data, enhancing the accuracy and frequency of monitoring Sustainable Development Goals (SDGs) indicators. One obstacle faced with LLMs is their high computational intensity, which can be a challenge for countries with limited resources. Addressing this challenge, SATGPT integrates LLM capabilities with cloud computing platforms and EO data to develop a functional spatial decision support system for swift deployment in resource-constrained settings. A unique feature is that SATGPT is not a blackbox instead, users are empowered to access data sources to independently judge the AI accuracy. #LLM #CloudComputing #AI #SustainableDevelopment https://lnkd.in/gJ4_7bzc

I recently worked on this article to explain my perspective on how LLMs are changing multi-agent systems: https://lnkd.in/d7bQxjsZ

Unleashing the Power of LLMs with Parameter-Efficient Fine-Tuning