​[[{“value”:” The last couple of years have seen tremendous development in Artificial Intelligence with the advent of Large Language Models (LLMs). These models have emerged as potent tools in a myriad of applications, particularly in complex reasoning tasks. Trained on vast datasets, LLMs can comprehend… Read More »ATF: An Analysis-to-Filtration Prompting Method for Enhancing LLM Reasoning in the Presence of Irrelevant Information Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Language models have gained prominence in reinforcement learning from human feedback (RLHF), but current reward modeling approaches face challenges in accurately capturing human preferences. Traditional reward models, trained as simple classifiers, struggle to perform explicit reasoning about response quality, limiting their effectiveness in guiding… Read More »Improving RLHF (Reinforcement Learning from Human Feedback) with Critique-Generated Reward Models Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The rapid integration of AI technologies in medical education has revealed significant limitations in existing educational tools. Current AI-assisted systems primarily support solitary learning and are unable to replicate the interactive, multidisciplinary, and collaborative nature of real-world medical training. This deficiency poses a significant… Read More »Revolutionizing Medical Training with AI- This AI Paper Unveils MEDCO: Medical Education Copilots Based on a Multi-Agent Framework Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Advanced Machine Learning models called Graph Neural Networks (GNNs) process and analyze graph-structured data. They have proven quite successful in a number of applications, including recommender systems, question-answering, and chemical modeling. Transductive node classification is a typical problem for GNNs, where the goal is… Read More »Training-Free Graph Neural Networks (TFGNNs) with Labels as Features (Laf) for Superior Transductive Learning Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Creating cutting-edge, interactive applications for the terminal takes a lot of work. Although powerful, terminal-based apps frequently need more sophisticated user interfaces of web or desktop programs. Within the confines of a terminal, developers must create functional and aesthetically pleasing applications. The flexibility and… Read More »Textual: ARapid Application Development Framework for Python Niharika Singh Artificial Intelligence Category – MarkTechPost

​[[{“value”:” LinkedIn has recently unveiled its groundbreaking innovation, the Liger (LinkedIn GPU Efficient Runtime) Kernel, a collection of highly efficient Triton kernels designed specifically for large language model (LLM) training. This new technology represents an advancement in machine learning, particularly in training large-scale models that… Read More »LinkedIn Released Liger (Linkedin GPU Efficient Runtime) Kernel: A Revolutionary Tool That Boosts LLM Training Efficiency by Over 20% While Cutting Memory Usage by 60% Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Retrieval-Augmented Generation (RAG) has faced significant challenges in development, including a lack of comprehensive comparisons between algorithms and transparency issues in existing tools. Popular frameworks like LlamaIndex and LangChain have been criticized for excessive encapsulation, while lighter alternatives such as FastRAG and RALLE offer… Read More »RAGLAB: A Comprehensive AI Framework for Transparent and Modular Evaluation of Retrieval-Augmented Generation Algorithms in NLP Research Shoaib Nazir Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Language Foundation Models (LFMs) and Large Language Models (LLMs) have demonstrated their ability to handle multiple tasks efficiently with a single fixed model. This achievement has motivated the development of Image Foundation Models (IFMs) in computer vision, which aim to encode general information from… Read More »TWLV-I: A New Video Foundation Model that Constructs Robust Visual Representations for both Motion and Appearance-based Videos Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Retrieval Augmented Generation (RAG) represents a cutting-edge advancement in Artificial Intelligence, particularly in NLP and Information Retrieval (IR). This technique is designed to enhance the capabilities of Large Language Models (LLMs) by seamlessly integrating contextually relevant, timely, and domain-specific information into their responses. This… Read More »AWS Enhancing Information Retrieval in Large Language Models: A Data-Centric Approach Using Metadata, Synthetic QAs, and Meta Knowledge Summaries for Improved Accuracy and Relevancy Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The Mixture of Experts (MoE) models enhance performance and computational efficiency by selectively activating subsets of model parameters. While traditional MoE models utilize homogeneous experts with identical capacities, this approach limits specialization and parameter utilization, especially when handling varied input complexities. Recent studies highlight… Read More »Heterogeneous Mixture of Experts (HMoE): Enhancing Model Efficiency and Performance with Diverse Expert Capacities Sana Hassan Artificial Intelligence Category – MarkTechPost