Large Language Models (LLMs) have grown in complexity and demand, creating significant challenges for companies aiming to provide scalable and cost-effective Model-as-a-Service (MaaS). The rapid adoption of LLMs in various applications has led to highly variable workloads in terms of input/output lengths, arrival frequencies, and service requirements. Balancing resource utilization to meet these diverse needs… →

The rapid advancement of large language models (LLMs) has exposed critical infrastructure challenges in model deployment and communication. As models scale in size and complexity, they encounter significant storage, memory, and network bandwidth bottlenecks. The exponential growth of model sizes creates computational and infrastructural strains, particularly in data transfer and storage mechanisms. Current models like… →

Large language models are good at many tasks but bad at complex reasoning, especially when it comes to math problems. Current In-Context Learning (ICL) methods depend heavily on carefully chosen examples and human help, which makes it hard to handle new problems. Traditional methods also use straightforward reasoning techniques that limit their ability to look… →

Large Language Models (LLMs) are powerful tools for various applications due to their knowledge and understanding capabilities. However, they are also vulnerable to exploitation, especially in jailbreaking attacks in multi-round dialogues. Jailbreaking attacks exploit the complex and sequential nature of human-LLM interactions to subtly manipulate the model’s responses over multiple exchanges. By carefully building questions… →

Developing web agents is a challenging area of AI research that has attracted significant attention in recent years. As the web becomes more dynamic and complex, it demands advanced capabilities from agents that interact autonomously with online platforms. One of the major challenges in building web agents is effectively testing, benchmarking, and evaluating their behavior… →

Allen Institute for AI (AI2) was founded in 2014 and has consistently advanced artificial intelligence research and applications. OLMo is a large language model (LLM) introduced in February 2024. Unlike proprietary models, OLMo is fully open-source, with its pre-training data, training code, and model weights freely available to the public. This transparency is designed to… →

The detailed study of the fly connectome has revolutionized neuroscience, offering insights into brain circuitry and its applications. Extending this progress to the mouse brain, which shares more structural similarities with the human brain, holds immense potential. It could provide the foundation for brain-inspired AI systems, enabling human-like capabilities such as continual learning, energy efficiency,… →