Benchmarking large language models presents some unusual challenges. For one, the main purpose of many LLMs is to provide compelling text that’s indistinguishable from human writing. And success in ...

More information: Mingzhen Lu et al, A first-principles mathematical model integrates the disparate timescales of human learning, npj Complexity (2025). DOI: 10.1038/s44260-025-00039-x ...

But it’s not math that we can follow. “Open up a large language model and all you will see is billions of numbers—the parameters,” says Batson. “It’s not illuminating.” ...

Tech & Science Reimagining large language model workflows in a cloud-driven era By Jon Stojan Published March 21, 2025 Photo courtesy of Vincent Kanka ...

TAIPEI, March 10 (Reuters) - Taiwan’s Foxconn (2317.TW), opens new tab said on Monday it has launched its first large language model and plans to use the technology to improve manufacturing and ...

After training a machine learning model on the patients’ functional brain images and clinical assessments, the researchers found that the model was able to predict an individual’s PTSD symptom ...

ByteDance’s Doubao Large Model team yesterday introduced UltraMem, a new architecture designed to address the high memory access issues found during inference in Mixture of Experts (MoE) models.

Reinforcement learning (RL) is crucial for improving reasoning in large language models (LLMs), complementing supervised fine-tuning (SFT) to enhance accuracy, consistency, and response clarity.

DeepSeek-R1, a new reasoning model made by Chinese researchers, completes tasks with a comparable proficiency to OpenAI's o1 at a fraction of the cost.

Self-Supervised Learning: Self-supervised learning involves training models on large volumes of unlabeled data using extrapolation techniques that allow the model to guess the next word in a phrase.