Training deep learning DL models is time-consuming and unpredictable. It is often hard to know precisely when a model will finish training or if it might crash unexpectedly. This uncertainty can lead to inefficiencies, especially when monitoring training manually. Some solutions exist to manage training times and failures, such as early stopping techniques and logging…
Modern Deep Neural Networks (DNNs) are inherently opaque; we do not know how or why these computers arrive at the predictions they do. This is a major barrier to the broader use of Machine Learning techniques in many domains. An emerging area of study called Explainable AI (XAI) has arisen to shed light on how…
Large Language Models (LLMs) have significantly advanced in recent times, primarily because of their increased capacity to follow human commands efficiently. Reinforcement Learning from Human Feedback (RLHF) is the main technique for matching LLMs to human intent. This method operates by optimizing a reward function, which can be reparameterized within the LLM’s policy or be…
Sparse neural networks aim to optimize computational efficiency by reducing the number of active weights in the model. This technique is vital as it addresses the escalating computational costs associated with training and inference in deep learning. Sparse networks enhance performance without dense connections, reducing computational resources and energy consumption. The main problem addressed in…
LLMs need to generate text reflecting the diverse views of multifaceted personas. Prior studies on bias in LLMs have focused on simplistic, one-dimensional personas or multiple-choice formats. However, many applications require LLMs to generate open-ended text based on complex personas. The ability to steer LLMs to represent these multifaceted personas accurately is critical to avoid…
AI legal research and document drafting tools promise to enhance efficiency and accuracy in performing complex legal tasks. However, these tools need help with their reliability in producing accurate legal information. Lawyers increasingly use AI to augment their practice, from drafting contracts to analyzing discovery productions and conducting legal research. As of January 2024, 41…
In recent research, a team of researchers from IEIT Systems has developed Yuan 2.0-M32, a sophisticated model built using the Mixture of Experts (MoE) architecture. Similar in base design to Yuan-2.0 2B, it is distinguished by its use of 32 experts. The model has an efficient computational structure because only two of these experts are…
Artificial intelligence is continually evolving, focusing on optimizing algorithms to improve the performance and efficiency of large language models (LLMs). Reinforcement learning from human feedback (RLHF) is a significant area within this field, aiming to align AI models with human values and intentions to ensure they are helpful, honest, and safe. One of the primary…
Hugging Face has introduced FineWeb, a comprehensive dataset designed to enhance the training of large language models (LLMs). Published on May 31, 2024, this dataset sets a new benchmark for pretraining LLMs, promising improved performance through meticulous data curation and innovative filtering techniques. FineWeb draws from 96 CommonCrawl snapshots, encompassing a staggering 15 trillion tokens…
Large language models (LLMs) possess advanced language understanding, enabling a shift in application development where AI agents communicate with LLMs via natural language prompts to complete tasks collaboratively. Applications like Microsoft Teams and Google Meet use LLMs to summarize meetings, while search engines like Google and Bing enhance their capabilities with chat features. These LLM-based…