Large language models (LLMs) have found applications in diverse industries, automating tasks and enhancing decision-making. However, when applied to specialized domains like chip design, they face unique challenges. Domain-adapted models, such as NVIDIA’s ChipNeMo, often struggle with instruction alignment—the ability to follow precise human commands. This limitation reduces their effectiveness in tasks like generating accurate… →

Artificial Intelligence (AI) is now an integral ingredient in automating tasks in various industries, gaining immense efficiency and better decision-making benefits. Autonomy in agents has developed the capability to work independently to achieve specific functionalities, such as controlling smart home appliances or managing data in complex systems. The idea behind these autonomy features is to… →

LLMs have demonstrated impressive capabilities in answering medical questions accurately, even outperforming average human scores in some medical examinations. However, their adoption in medical documentation tasks, such as clinical note generation, faces challenges due to the risk of generating incorrect or inconsistent information. Studies reveal that 20% of patients reading clinical notes identified errors, with… →

We have established notable milestones in AI understanding over the past decade, especially with rapid research booming in deep learning. However, much of the ocean remains unexplored, and the shore still seems far off for real-world applications. Every moment, researchers from different parts of the world aspire and strive to innovate AI solutions that are… →

Generative language models face persistent challenges when transitioning from training to practical application. One significant difficulty lies in aligning these models to perform optimally during inference. Current methods, such as Reinforcement Learning from Human Feedback (RLHF), focus on improving win rates against a baseline model. However, they often overlook the role of inference-time decoding strategies… →