​ The inherent capabilities of pretrained large language models are notable, yet achieving desired behaviors often requires additional adaptation. When dealing with models whose weights are kept private, the challenge intensifies, rendering tuning either excessively costly or outright impossible. As a result, striking the right… Read More »Researchers from the University of Washington and Allen Institute for AI Present Proxy-Tuning: An Efficient Alternative to Finetuning Large Language Models Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​ Large Language Models (LLMs) have exhibited remarkable prowess across various natural language processing tasks. However, applying them to Information Retrieval (IR) tasks remains a challenge due to the scarcity of IR-specific concepts in natural language. Addressing this, the idea of instruction tuning has emerged… Read More »This AI Paper from China Introduces a Groundbreaking Approach to Enhance Information Retrieval with Large Language Models Using the INTERS Dataset Vineet Kumar Artificial Intelligence Category – MarkTechPost

​ Stable AI has recently released a new state-of-the-art model, Stable-Code-3B, designed for code completion in various programming languages with multiple additional capabilities. The model is a follow-up on the Stable Code Alpha 3B. It is trained on 1.3 trillion tokens including both natural language… Read More »Stability AI Releases Stable Code 3B: A 3 Billion Parameter Large Language Model (LLM) that Allows Accurate and Responsive Code Completion Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​ Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, offering remarkable capabilities in processing and generating language-based responses. LLMs are being used in many applications, from automated customer service to generating creative content. However, one critical challenge surfacing with using… Read More »EASYTOOL: An Artificial Intelligence Framework Transforming Diverse and Lengthy Tool Documentation into a Unified and Concise Tool Instruction for Easier Tool Usage Muhammad Athar Ganaie Artificial Intelligence Category – MarkTechPost

​ Mixture-of-Experts (MoE) is an architecture based on the “divide and conquer” principle to solve complex tasks. Multiple individual machine learning (ML) models (called experts) work individually based on their specializations to provide the most optimal results. To better understand their use cases, Mistral AI… Read More »Fireworks AI Introduces FireAttention: A Custom CUDA Kernel Optimized for Multi-Query Attention Models Asif Razzaq Artificial Intelligence Category – MarkTechPost

​ The Natural Language Generation (NLG) field stands at the intersection of linguistics and artificial intelligence. It focuses on the creation of human-like text by machines. Recent advancements in Large Language Models (LLMs) have revolutionized NLG, significantly enhancing the ability of systems to generate coherent… Read More »Assessing Natural Language Generation (NLG) in the Age of Large Language Models: A Comprehensive Survey and Taxonomy Adnan Hassan Artificial Intelligence Category – MarkTechPost

​ The emergence of large language models (LLMs) like GPT, Claude, Gemini, LLaMA, Mistral, etc., has greatly accelerated recent advances in natural language processing (NLP). Instruction tweaking is a well-known approach to training LLMs. This method allows LLMs to improve their pre-trained representations to follow… Read More »Parameter-Efficient Sparsity Crafting (PESC): A Novel AI Approach to Transition Dense Models to Sparse Models Using a Mixture-of-Experts (Moe) Architecture Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​ The practical deployment of multi-billion parameter neural rankers in real-world systems poses a significant challenge in information retrieval (IR). These advanced neural rankers demonstrate high effectiveness but are hampered by their substantial computational requirements for inference, making them impractical for production use. This dilemma… Read More »Can We Optimize AI for Information Retrieval with Less Compute? This AI Paper Introduces InRanker: a Groundbreaking Approach to Distilling Large Neural Rankers Nikhil Artificial Intelligence Category – MarkTechPost

​ In the constantly evolving field of machine learning, particularly in semantic segmentation, the accurate estimation and validation of uncertainty have become increasingly vital. Despite numerous studies claiming advances in uncertainty methods, there remains a disconnection between theoretical development and practical application. Fundamental questions linger,… Read More »This AI Paper from Germany Proposes ValUES: An Artificial Intelligence Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation Sana Hassan Artificial Intelligence Category – MarkTechPost

​ Efficiently handling complex, high-dimensional data is crucial in data science. Without proper management tools, data can become overwhelming and hinder progress. Prioritizing the development of effective strategies is imperative to leverage data’s full potential and drive real-world impact. Traditional database management systems falter under… Read More »Faiss: A Machine Learning Library Dedicated to Vector Similarity Search, a Core Functionality of Vector Databases Muhammad Athar Ganaie Artificial Intelligence Category – MarkTechPost