The phenomenon of "model collapse" represents a significant challenge in artificial intelligence (AI) research, particularly impacting large language models (LLMs). When these models are continually trained on data created by earlier versions of similar models, they lose their ability to accurately represent the underlying data distribution, deteriorating in effectiveness over successive generations.
Current training methods of…
The rapid development of Transformer models in natural language processing (NLP) has brought about significant challenges, particularly with memory requirements for the training of these large-scale models. A new paper addresses these issues by presenting a new methodology called MINI-SEQUENCE TRANSFORMER (MST) which optimizes memory usage during long-sequence training without compromising performance.
Traditional approaches such as…
OuteAI has released two new models of its Lite series, namely Lite-Oute-1-300M and Lite-Oute-1-65M, which are designed to maintain optimum efficiency and performance, making them suitable for deployment across various devices. The Lite-Oute-1-300M model is based on the Mistral architecture and features 300 million parameters, while the Lite-Oute-1-65M, based on the LLaMA architecture, hosts around…
Researchers from MIT and the University of Washington have developed a model that predicts human behavior by considering computational constraints that limit an individual's problem-solving ability. This model can be used to estimate a person's ‘inference budget’, or time available for problem-solving, based on their past actions. It can then predict their future behavior.
Drawing from…
