Artificial intelligence (AI) focuses on creating systems capable of performing tasks requiring human intelligence. Within this field, the development of large language models (LLMs) aims to understand and generate human language, with applications in translation, summarization, and question-answering. Despite these advancements, complex multi-step reasoning tasks, such as solving mathematical problems, still need to be solved… →

Large language models (LLMs) like transformers are typically pre-trained with a fixed context window size, such as 4K tokens. However, many applications require processing much longer contexts, up to 256K tokens. Extending the context length of these models poses challenges, particularly in ensuring efficient use of information from the middle part of the context, often… →

Gradient descent-trained neural networks operate effectively even in overparameterized settings with random weight initialization, often finding global optimum solutions despite the non-convex nature of the problem. These solutions, achieving zero training error, surprisingly do not overfit in many cases, a phenomenon known as “benign overfitting.” However, for ReLU networks, interpolating solutions can lead to overfitting.… →

Large Language Models (LLMs) face challenges in capturing complex long-term dependencies and achieving efficient parallelization for large-scale training. Attention-based models have dominated LLM architectures due to their ability to address these issues. However, they struggle with computational complexity and extrapolation to longer sequences. State Space Models (SSMs) have emerged as a promising alternative, offering linear… →

Artificial intelligence (AI) is focused on developing systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding. These technologies have various applications across various industries, including healthcare, finance, transportation, and entertainment, making it a vital area of research and development. A significant challenge in AI is… →

Artificial intelligence’s large language models (LLMs) have become essential tools due to their ability to process and generate human-like text, enabling them to perform various tasks. These models rely heavily on high-quality instruction datasets for fine-tuning, which enhances their ability to understand and follow complex instructions. The success of LLMs in various applications, from chatbots… →

Large language models (LLMs) face a significant challenge in accurately representing uncertainty over the correctness of their output. This issue is critical for decision-making applications, particularly in fields like healthcare where erroneous confidence can lead to dangerous outcomes. The task is further complicated by linguistic variances in freeform generation, which cannot be exhaustively accounted for… →

NVIDIA has recently unveiled the Nemotron-4 340B, a groundbreaking family of models designed to generate synthetic data for training large language models (LLMs) across various commercial applications. This release marks a significant advancement in generative AI, offering a comprehensive suite of tools optimized for NVIDIA NeMo and NVIDIA TensorRT-LLM and includes cutting-edge instruct and reward… →

As AI-generated data increasingly supplements or even replaces human-annotated data, concerns have arisen about the degradation in model performance when models are iteratively trained on synthetic data. Model collapse refers to this phenomenon where a model’s performance deteriorates significantly when trained on synthesized data generated using the model. This problem is significant because it hinders… →