Databases are essential for storing and retrieving structured data supporting business intelligence, research, and enterprise applications. Querying databases typically requires SQL, which varies ...

Large Language Models (LLMs) are primarily designed for text-based tasks, limiting their ability to interpret and generate multimodal content such as images, videos, and audio. Conventionally, ...

Chemical reasoning involves intricate, multi-step processes requiring precise calculations, where small errors can lead to significant issues. LLMs often struggle with domain-specific challenges, such ...

Large language models (LLMs) have revolutionized artificial intelligence by demonstrating remarkable capabilities in text generation and problem-solving. However, a critical limitation persists in ...

Edge devices like smartphones, IoT gadgets, and embedded systems process data locally, improving privacy, reducing latency, and enhancing responsiveness, and AI is getting integrated into these ...

Large language models (LLMs) have revolutionized artificial intelligence by demonstrating remarkable capabilities in text generation and problem-solving. However, a critical limitation persists in ...

Edge devices like smartphones, IoT gadgets, and embedded systems process data locally, improving privacy, reducing latency, and enhancing responsiveness, and AI is getting integrated ...

Large language models (LLMs) have revolutionized artificial intelligence by demonstrating remarkable capabilities in text generation and problem-solving. However, a critical limitation persists in ...

Large-scale language models (LLMs) have advanced the field of artificial intelligence as they are used in many applications. Although they can almost perfectly simulate human language, they tend to ...

Large Language Models (LLMs) have demonstrated notable reasoning capabilities in mathematical problem-solving, logical inference, and programming. However, their effectiveness is often contingent on ...

Traditional approaches to training language models heavily rely on supervised fine-tuning, where models learn by imitating correct responses. While effective for basic tasks, this method limits a ...

Regression tasks, which involve predicting continuous numeric values, have traditionally relied on numeric heads such as Gaussian parameterizations or pointwise tensor projections. These traditional ...