Test-time aggregation strategies, such as generating and combining multiple answers, can enhance LLM performance but eventually hit diminishing returns. Refinement, where model feedback is used to ...

In artificial intelligence and natural language processing, long-context reasoning has emerged as a crucial area of research. As the volume of information that needs to be processed grows, machines ...

Large language models (LLMs) have gained significant attention due to their potential to enhance various artificial intelligence applications, particularly in natural language processing. When ...

Transformer models have revolutionized sequence modeling tasks, but their standard attention mechanism faces significant challenges when dealing with long sequences. The quadratic complexity of ...

Artificial intelligence (AI) research has increasingly focused on enhancing the efficiency & scalability of deep learning models. These models have revolutionized natural language processing, computer ...

Large language models (LLMs) have become a pivotal part of artificial intelligence, enabling systems to understand, generate, and respond to human language. These models are used across various ...

AI Control assesses the safety of deployment protocols for untrusted AIs through red-teaming exercises involving a protocol designer and an adversary. AI systems, like chatbots with access to tools ...

Governments and humanitarian organizations need reliable data on building and infrastructure changes over time to manage urbanization, allocate resources, and respond to crises. However, many regions ...

Using advanced artificial intelligence models, video generation involves creating moving images from textual descriptions or static images. This area of research seeks to produce high-quality, ...

Multimodal large language models (MLLMs) focus on creating artificial intelligence (AI) systems that can interpret textual and visual data seamlessly. These models aim to bridge the gap between ...

Chain-of-thought (CoT) prompting has emerged as a popular technique to enhance large language models’ (LLMs) problem-solving abilities by generating intermediate steps. Despite its better performance ...

Personalization is essential in many language tasks, as users with similar needs may prefer different outputs based on personal preferences. Traditional methods involve fine-tuning language models for ...