The rapid advancements in artificial intelligence have opened new possibilities, but the associated costs often limit who can benefit from these technologies. Large-scale models ...

The rapid advancements in artificial intelligence have opened new possibilities, but the associated costs often limit who can benefit from these technologies. Large-scale models ...

Developing effective multi-modal AI systems for real-world applications requires handling diverse tasks such as fine-grained recognition, visual grounding, reasoning, and multi-step problem-solving.

Artificial Intelligence (AI) has made significant strides in various fields, including healthcare, finance, and education. However, its adoption is not without challenges. Concerns about ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Large language models (LLMs) have recently been enhanced through retrieval-augmented generation (RAG), which dynamically integrates external knowledge sources to improve response quality for ...

Time-series forecasting plays a crucial role in various domains, including finance, healthcare, and climate science. However, achieving accurate predictions remains a significant challenge.