marktechpost6d
LASR: A Novel Machine Learning Approach to Symbolic Regression Using Large Language Models
Symbolic regression is an advanced computational method to find mathematical equations that best explain a dataset. Unlike traditional regression, which fits data to predefined models, symbolic ...
marktechpost6d
Microsoft Releases GRIN MoE: A Gradient-Informed Mixture of Experts MoE Model for Efficient and Scalable Deep Learning
Artificial intelligence (AI) research has increasingly focused on enhancing the efficiency & scalability of deep learning models. These models have revolutionized natural language processing, computer ...
marktechpost5d
Michelangelo: An Artificial Intelligence Framework for Evaluating Long-Context Reasoning in Large Language Models Beyond Simple Retrieval Tasks
In artificial intelligence and natural language processing, long-context reasoning has emerged as a crucial area of research. As the volume of information that needs to be processed grows, machines ...
marktechpost6d
ByteDance Researchers Release InfiMM-WebMath-40B: An Open Multimodal Dataset Designed for Complex Mathematical Reasoning
Artificial intelligence has significantly enhanced complex reasoning tasks, particularly in specialized domains such as mathematics. Large Language Models (LLMs) have gained attention for their ...
marktechpost6d
Gated Slot Attention: Advancing Linear Attention Models for Efficient and Effective Language Processing
Transformer models have revolutionized sequence modeling tasks, but their standard attention mechanism faces significant challenges when dealing with long sequences. The quadratic complexity of ...
marktechpost5d
Chain-of-Thought (CoT) Prompting: A Comprehensive Analysis Reveals Limited Effectiveness Beyond Math and Symbolic Reasoning
Chain-of-thought (CoT) prompting has emerged as a popular technique to enhance large language models’ (LLMs) problem-solving abilities by generating intermediate steps. Despite its better performance ...
marktechpost5d
Exploring Input Space Mode Connectivity: Insights into Adversarial Detection and Deep Neural Network Interpretability
Input space mode connectivity in deep neural networks builds upon research on excessive input invariance, blind spots, and connectivity between inputs yielding similar outputs. The phenomenon exists ...
marktechpost4d
Trust-Align: An AI Framework for Improving the Trustworthiness of Retrieval-Augmented Generation in Large Language Models
Large language models (LLMs) have gained significant attention due to their potential to enhance various artificial intelligence applications, particularly in natural language processing. When ...
marktechpost5d
RAG, AI Agents, and Agentic RAG: An In-Depth Review and Comparative Analysis of Intelligent AI Systems
Artificial intelligence (AI) has given rise to powerful models capable of performing diverse tasks. Two of the most impactful advancements in this space are Retrieval-Augmented Generation (RAG) and ...
marktechpost4d
MAGICORE: An AI Framework for Multi Agent Iteration for Coarse-to-fine Refinement
Test-time aggregation strategies, such as generating and combining multiple answers, can enhance LLM performance but eventually hit diminishing returns. Refinement, where model feedback is used to ...
marktechpost4d
Diffusion Reuse MOtion (Dr. Mo): A Diffusion Model for Efficient Video Generation with Motion Reuse
Using advanced artificial intelligence models, video generation involves creating moving images from textual descriptions or static images. This area of research seeks to produce high-quality, ...
marktechpost4d
Enhancing Large Language Models with Diverse Instruction Data: A Clustering and Iterative Refinement Approach
Large language models (LLMs) have become a pivotal part of artificial intelligence, enabling systems to understand, generate, and respond to human language. These models are used across various ...