Large Language Model

The rapid increase in available scientific literature presents a challenging environment for researchers. Current Language Learning Models (LLMs) are proficient at extracting text-based information but struggle with important multimodal data, including charts and molecular structures, found in scientific texts. In response to this problem, researchers from DP Technology and AI for Science Institute, Beijing, have…

Large language models (LLMs) have emerged as powerful tools in artificial intelligence, providing improvements in areas such as conversational AI and complex analytical tasks. However, while these models have the capacity to sift through and apply extensive amounts of data, they also face significant challenges, particularly in the field of 'knowledge conflicts'. Knowledge conflicts occur when…

Large language models (LLMs) have made significant strides in the field of artificial intelligence, paving the way for machines that understand and generate human-like text. However, these models face the inherent challenge of their knowledge being fixed at the point of their training, limiting their adaptability and ability to incorporate new information post-training. This proves…

Artificial intelligence (AI) researchers from Stanford University and Notbad AI Inc are striving to improve language models' AI capabilities in interpreting and generating nuanced, human-like text. Their project, called Quiet Self-Taught Reasoner (Quiet-STaR), embeds reasoning capabilities directly into language models. Unlike previous methods, which focused on training models using specific datasets for particular tasks, Quiet-STaR…

Computer vision, the field dealing with how computers can gain understanding from digital images or videos, has seen remarkable growth in recent years. A significant challenge within this field is the precise interpretation of intricate image details, understanding both global and local visual cues. Despite advances with conventional models like Convolutional Neural Networks (CNNs) and…

Scaling laws in artificial intelligence are fundamental in the development of Large Language Models (LLMs). These laws play the role of a director, coordinating the growth of models while revealing patterns of development that go beyond mere computation. With every new step, the models become more nuanced, accurately deciphering the complexities of human expression. Scaling…

In the realm of artificial intelligence, notable advancements are being made in the development of language agents capable of understanding and navigating human social dynamics. These sophisticated agents are being designed to comprehend and react to cultural nuances, emotional expressions, and unspoken social norms. The ultimate objective is to establish interactive AI entities that are…