Understanding implicit meaning is a fundamental aspect of human communication. Yet, current Natural Language Inference (NLI) models struggle to recognize implied entailments—statements that are logically inferred but not explicitly stated. Most current NLI datasets are focused on explicit entailments, making the models insufficiently equipped to deal with scenarios where meaning is indirectly expressed. This limitation… →
LLMs have demonstrated impressive cognitive abilities, making significant strides in artificial intelligence through their ability to generate and predict text. However, while various benchmarks evaluate their perception, reasoning, and decision-making, less attention has been given to their exploratory capacity. Exploration, a key aspect of intelligence in humans and AI, involves seeking new information and adapting… →
Currently, three trending topics in the implementation of AI are LLMs, RAG, and Databases. These enable us to create systems that are suitable and specific to our use. This AI-powered system, combining a vector database and AI-generated responses, has applications across various industries. In customer support, AI chatbots retrieve knowledge base answers dynamically. The legal… →
Multi-vector retrieval has emerged as a critical advancement in information retrieval, particularly with the adoption of transformer-based models. Unlike single-vector retrieval, which encodes queries and documents as a single dense vector, multi-vector retrieval allows for multiple embeddings per document and query. This approach provides a more granular representation, improving search accuracy and retrieval quality. Over… →
Large language models (LLMs) have become indispensable for various natural language processing applications, including machine translation, text summarization, and conversational AI. However, their increasing complexity and size have led to significant computational efficiency and memory consumption challenges. As these models grow, the resource demand makes them difficult to deploy in environments with limited computational capabilities.… →
Developing AI agents capable of independent decision-making, especially for multi-step tasks, is a significant challenge. DeepSeekAI, a leader in advancing large language models and reinforcement learning, focuses on enabling AI to process information, predict outcomes, and adjust actions as situations evolve. It underlines the importance of proper reasoning in dynamic settings. The new development from… →
BACKGROUND: Children with autism spectrum disorder (ASD) are known to experience difficulties in coordinating fine- and gross-motor movement. Previous interventional studies have reported significant effect of exercise-based intervention programs on improving motor skills and alleviating symptoms in ASD; however, researchers are yet to know why some participants experienced less improvement than others. One plausible explanation… →
CONCLUSIONS: Short-term reduction of FFA concentration does not improve insulin-stimulated vasodilation in patients with metabolic syndrome. →
Developing compact yet high-performing language models remains a significant challenge in artificial intelligence. Large-scale models often require extensive computational resources, making them inaccessible for many users and organizations with limited hardware capabilities. Additionally, there is a growing demand for methods that can handle diverse tasks, support multilingual communication, and provide accurate responses efficiently without sacrificing… →
Structure-from-motion (SfM) focuses on recovering camera positions and building 3D scenes from multiple images. This process is important for tasks like 3D reconstruction and novel view synthesis. A major challenge comes from processing large image collections efficiently while maintaining accuracy. Several approaches rely on the optimization of camera poses and scene geometry. However, these have… →