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​[[{“value”:” Autoregressive pre-training has proved to be revolutionary in machine learning, especially concerning sequential data processing. Predictive modeling of the following sequence elements has been highly effective in natural language processing and, increasingly, has been explored within computer vision domains. Video modeling is one area… Read More »This AI Paper Introduces Toto: Autoregressive Video Models for Unified Image and Video Pre-Training Across Diverse Tasks Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) like GPT-4, PaLM, Bard, and Copilot have made a huge impact in natural language processing (NLP). They can generate text, solve problems, and carry out conversations with remarkable accuracy. However, they also come with significant challenges. These models require vast… Read More »What are Small Language Models (SLMs)? Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Multi-modal Large Language Models (MLLMs) have revolutionized various image and video-related tasks, including visual question answering, narrative generation, and interactive editing. A critical challenge in this field is achieving fine-grained video content understanding, which involves pixel-level segmentation, tracking with language descriptions, and performing visual… Read More »Sa2VA: A Unified AI Framework for Dense Grounded Video and Image Understanding through SAM-2 and LLaVA Integration Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have revolutionized generative AI, showing remarkable capabilities in producing human-like responses. However, these models face a critical challenge known as hallucination, the tendency to generate incorrect or irrelevant information. This issue poses significant risks in high-stakes applications such as medical… Read More »RAG-Check: A Novel AI Framework for Hallucination Detection in Multi-Modal Retrieval-Augmented Generation Systems Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Understanding and processing human language has always been a difficult challenge in artificial intelligence. Early AI systems often struggled to handle tasks like translating languages, generating meaningful text, or answering questions accurately. These systems relied on rigid rules or basic statistical methods that couldn’t… Read More »What are Large Language Model (LLMs)? Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have shown remarkable capabilities across diverse natural language processing tasks, from generating text to contextual reasoning. However, their efficiency is often hampered by the quadratic complexity of the self-attention mechanism. This challenge becomes particularly pronounced with longer input sequences, where… Read More »SepLLM: A Practical AI Approach to Efficient Sparse Attention in Large Language Models Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Multi-hop queries have always given LLM agents a hard time with their solutions, necessitating multiple reasoning steps and information from different sources. They are crucial for analyzing a model’s comprehension, reasoning, and function-calling capabilities. At this time when new large models are booming every… Read More »ToolHop: A Novel Dataset Designed to Evaluate LLMs in Multi-Hop Tool Use Scenarios Adeeba Alam Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The rise of multimodal applications has highlighted the importance of instruction data in training MLMs to handle complex image-based queries effectively. Current practices for generating such data rely on LLMs or MLMs, which, despite their effectiveness, face several challenges. These include high costs, licensing… Read More »ProVision: A Scalable Programmatic Approach to Vision-Centric Instruction Data for Multimodal Language Models Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Artificial General Intelligence (AGI) seeks to create systems that can perform various tasks, reasoning, and learning with human-like adaptability. Unlike narrow AI, AGI aspires to generalize its capabilities across multiple domains, enabling machines to operate in dynamic and unpredictable environments. Achieving this requires combining… Read More »This AI Paper Explores Embodiment, Grounding, Causality, and Memory: Foundational Principles for Advancing AGI Systems Nikhil Artificial Intelligence Category – MarkTechPost