​[[{“value”:” Retrieval Augmented Generation (RAG) is an AI framework that optimizes the output of a Large Language Model (LLM) by referencing a credible knowledge base outside of its training sources. RAG combines the capabilities of LLMs with the strengths of traditional information retrieval systems such… Read More »Enhancing Language Models with Retrieval-Augmented Generation: A Comprehensive Guide Shobha Kakkar Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Cardinality estimation (CE) is essential to many database-related tasks, such as query generation, cost estimation, and query optimization. Accurate CE is necessary to ensure optimal query planning and execution within a database system. Adopting machine learning (ML) techniques has introduced new possibilities for CE,… Read More »AutoCE: An Intelligent Model Advisor Revolutionizing Cardinality Estimation for Databases through Advanced Deep Metric Learning and Incremental Learning Techniques Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have gained significant attention in machine learning, shifting the focus from optimizing generalization on small datasets to reducing approximation error on massive text corpora. This paradigm shift presents researchers with new challenges in model development and training methodologies. The primary… Read More »Scaling Laws and Model Comparison: New Frontiers in Large-Scale Machine Learning Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Artificial intelligence (AI) is transforming rapidly, particularly in multimodal learning. Multimodal models aim to combine visual and textual information to enable machines to understand and generate content that requires inputs from both sources. This capability is vital for tasks such as image captioning, visual… Read More »Ovis-1.6: An Open-Source Multimodal Large Language Model (MLLM) Architecture Designed to Structurally Align Visual and Textual Embeddings Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Language models have become a cornerstone of modern NLP, enabling significant advancements in various applications, including text generation, machine translation, and question-answering systems. Recent research has focused on scaling these models in terms of the amount of training data and the number of parameters.… Read More »MassiveDS: A 1.4 Trillion-Token Datastore Enabling Language Models to Achieve Superior Efficiency and Accuracy in Knowledge-Intensive NLP Applications Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) are designed to understand and manage complex language tasks by capturing context and long-term dependencies. A critical factor for their performance is the ability to handle long-context inputs, which allows for a deeper understanding of content over extensive text sequences.… Read More »This AI Paper Introduces a Novel L2 Norm-Based KV Cache Compression Strategy for Large Language Models Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Weight decay and ℓ2 regularization are crucial in machine learning, especially in limiting network capacity and reducing irrelevant weight components. These techniques align with Occam’s razor principles and are central to discussions on generalization bounds. However, recent studies have questioned the correlation between norm-based… Read More »Revisiting Weight Decay: Beyond Regularization in Modern Deep Learning Sajjad Ansari Artificial Intelligence Category – MarkTechPost