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Large language models (LLMs) have made significant leaps in natural language processing, demonstrating remarkable generalization capabilities across diverse tasks. However, due to inconsistent adherence to instructions, these models face a critical challenge in generating accurately formatted outputs, such as JSON. This limitation poses a significant hurdle for AI-driven applications requiring structured LLM outputs integrated into…
Large Language Models (LLMs) have gained significant attention due to their impressive performance, with the release of Llama 3.1 in July 2024 being a notable example. However, deploying these models in resource-constrained environments poses significant challenges due to their huge parameter count. Low-bit quantization has emerged as a popular technique to compress LLMs, reducing memory…
Traditional search engines have predominantly relied on text-based queries, limiting their ability to process and interpret the increasingly complex information found online today. Many modern websites feature both text and images. Yet, the ability of conventional search engines to handle these multimodal queries, those that require an understanding of both visual and textual content, remains…
In an era of AI-transforming industries, CodeMaker AI has achieved a landmark breakthrough by autonomously recreating a 90,000-line software library with an astounding 91% similarity to the original codebase. This achievement marks a significant shift in how AI can be utilized in software development, demonstrating the potential to reduce manual coding efforts and accelerate development…
Recommendation systems have become the foundation for personalized services across e-commerce, streaming, and social media platforms. These systems aim to predict user preferences by analyzing historical interactions, allowing platforms to suggest relevant items or content. The accuracy & effectiveness of these systems depends heavily on how well user and item characteristics are modeled. Over the…
The University of Washington and the Allen Institute for AI (Ai2) have recently made a significant contribution to the AI research community by releasing their cutting-edge language models: MagpieLM-4B-Chat-v0.1 and MagpieLM-8B-Chat-v0.1. Part of the larger MagpieLM project, these models are specifically designed to address the rising need for aligned language models that can perform advanced…
Multimodal large language models (MLLMs) focus on creating artificial intelligence (AI) systems that can interpret textual and visual data seamlessly. These models aim to bridge the gap between natural language understanding and visual comprehension, allowing machines to cohesively process various forms of input, from text documents to images. Understanding and reasoning across multiple modalities is…
Generative AI has emerged as a pivotal field with the rise of large language models (LLMs). These models are capable of producing complex outputs based on a variety of prompts. One notable area within this domain is Retrieval Augmented Generation (RAG), which integrates external information into LLMs to enhance factual accuracy. RAG specifically addresses the…
Efficient optimization of large-scale deep learning models remains a significant challenge as the cost of training large language models (LLMs) continues to escalate. As models grow larger, the computational burden and time required for training increase substantially, creating a demand for more efficient optimizers that can reduce both training time and resources. This challenge is…
Predicting the long-term behavior of chaotic systems, such as those used in climate modeling, is essential but requires significant computational resources due to the need for high-resolution spatiotemporal grids. One alternative to fully-resolved simulations (FRS) is to use coarse grids, with closure models correcting for errors by approximating the missing fine-scale information. While machine learning…