​[[{“value”:” Current multimodal retrieval-augmented generation (RAG) benchmarks primarily focus on textual knowledge retrieval for question answering, which presents significant limitations. In many scenarios, retrieving visual information is more beneficial or easier than accessing textual data. Existing benchmarks fail to adequately account for these situations, hindering… Read More »Researchers from UCLA and Stanford Introduce MRAG-Bench: An AI Benchmark Specifically Designed for Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In an era where large language models (LLMs) are becoming the backbone of countless applications—from customer support agents to productivity co-pilots—the need for robust, secure, and scalable infrastructure is more pressing than ever. Despite their transformative power, LLMs have several operational challenges that require… Read More »Meet Arch: The Intelligent Layer 7 Gateway for LLM Applications Shobha Kakkar Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have greatly advanced various natural language processing (NLP) tasks, but they often suffer from factual inaccuracies, particularly in complex reasoning scenarios involving multi-hop queries. Current Retrieval-Augmented Generation (RAG) techniques, especially those using open-source models, struggle to handle the complexity of… Read More »OPEN-RAG: A Novel AI Framework Designed to Enhance Reasoning Capabilities in RAG with Open-Source LLMs Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Generative AI models, driven by Large Language Models (LLMs) or diffusion techniques, are revolutionizing creative domains like art and entertainment. These models can generate diverse content, including texts, images, videos, and audio. However, refining the quality of outputs requires additional inference methods during deployment,… Read More »Google DeepMind Research Introduces Diversity-Rewarded CFG Distillation: A Novel Finetuning Approach to Enhance the Quality-Diversity Trade-off in Generative AI Models Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) often fail to consistently and accurately perform multi-step reasoning, especially in complex tasks like mathematical problem-solving and code generation. Despite recent advancements, LLMs struggle to detect and learn from errors because they are predominantly trained on correct solutions. This limitation… Read More »Salesforce AI Research Proposes Dataset-Driven Verifier to Improve LLM Reasoning Consistency Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have made significant progress in language generation, but their reasoning skills remain insufficient for complex problem-solving. Tasks such as mathematics, coding, and scientific questions continue to pose a significant challenge. Enhancing LLMs’ reasoning abilities is crucial for advancing their capabilities… Read More »OpenR: An Open-Source AI Framework Enhancing Reasoning in Large Language Models Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Mixture of Experts (MoE) models are becoming critical in advancing AI, particularly in natural language processing. MoE architectures differ from traditional dense models by selectively activating subsets of specialized expert networks for each input. This mechanism allows models to increase their capacity without proportionally… Read More »NVIDIA AI Researchers Explore Upcycling Large Language Models into Sparse Mixture-of-Experts Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” One of the most pressing challenges in the evaluation of Vision-Language Models (VLMs) is related to not having comprehensive benchmarks that assess the full spectrum of model capabilities. This is because most existing evaluations are narrow in terms of focusing on only one aspect… Read More »Holistic Evaluation of Vision Language Models (VHELM): Extending the HELM Framework to VLMs Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The current challenges in text-to-speech (TTS) systems revolve around the inherent limitations of autoregressive models and their complexity in aligning text and speech accurately. Many conventional TTS models require complex elements such as duration modeling, phoneme alignment, and dedicated text encoders, which add significant… Read More »F5-TTS: A Fully Non-Autoregressive Text-to-Speech System based on Flow Matching with Diffusion Transformer (DiT) Asif Razzaq Artificial Intelligence Category – MarkTechPost