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 ...

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 ...

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 ...

Large language models (LLMs) have gained widespread adoption due to their advanced text understanding and generation capabilities. However, ensuring their responsible behavior through safety alignment ...

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 ...

Large language models (LLMs) have become crucial in natural language processing, particularly for solving complex reasoning tasks. These models are designed to handle mathematical problem-solving, ...

In today’s fast-paced and interconnected world, mental health is more important than ever. The constant pressures of work, social media, and global events can take a toll on our emotional and ...

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 ...

The current challenges in text-to-speech (TTS) systems revolve around the inherent limitations of autoregressive models and their complexity in aligning text and speech accurately. Many conventional ...

Language models (LMs) are widely utilized across domains like mathematics, coding, and reasoning to handle complex tasks. These models rely on deep learning techniques to generate high-quality outputs ...

The problem with efficiently linearizing large language models (LLMs) is multifaceted. The quadratic attention mechanism in traditional Transformer-based LLMs, while powerful, is computationally ...

Model merging is an advanced technique in machine learning aimed at combining the strengths of multiple expert models into a single, more powerful model. This process allows the system to benefit from ...