Quantum computers are a revolutionary technology that harnesses the principles of quantum mechanics to perform calculations that would be infeasible for classical computers. Evaluating the performance of quantum computers has been a challenging task due to their sensitivity to noise, the complexity of quantum algorithms, and the limited availability of powerful quantum hardware. Decoherence and… →

Large language models (LLMs) have evolved to become powerful tools capable of understanding and responding to user instructions. Based on the transformer architecture, these models predict the next word or token in a sentence, generating responses with remarkable fluency. However, they typically respond without engaging in internal thought processes that could help improve the accuracy… →

Despite the vast accumulation of genomic data, the RNA regulatory code must still be better understood. Genomic foundation models, pre-trained on large datasets, can adapt RNA representations for biological prediction tasks. However, current models rely on training strategies like masked language modeling and next token prediction, borrowed from domains such as text and vision, which… →

Large Language Models (LLMs) need to be evaluated within the framework of embodied decision-making, i.e., the capacity to carry out activities in either digital or physical environments. Even with all of the research and applications that LLMs have seen in this field, there is still a gap in knowledge of their actual capabilities. A portion… →

The ever-increasing size of Large Language Models (LLMs) presents a significant challenge for practical deployment. Despite their transformative impact on natural language processing, these models are often hindered by high memory transfer requirements, which pose a bottleneck during autoregressive generation. This results in high energy consumption and substantial inference time, limiting their scalability and use… →

The increasing reliance on machine learning models for processing human language comes with several hurdles, such as accurately understanding complex sentences, segmenting content into comprehensible parts, and capturing the contextual nuances present in multiple domains. In this landscape, the demand for models capable of breaking down intricate pieces of text into manageable, proposition-level components has… →

Bias in AI-powered systems like chatbots remains a persistent challenge, particularly as these models become more integrated into our daily lives. A pressing issue concerns biases that can manifest when chatbots respond differently to users based on name-related demographic indicators, such as gender or race. Such biases can undermine trust, especially in name-sensitive contexts where… →

The rapid growth of large language models (LLMs) and their increasing computational requirements have prompted a pressing need for optimized solutions to manage memory usage and inference speed. As models like GPT-3, Llama, and other large-scale architectures push the limits of GPU capacity, efficient hardware utilization becomes crucial. High memory requirements, slow token generation, and… →