Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

The EMNLP acceptances follow a third-place finish and two paper acceptances at an ICML 2026 workshop in July, further ...

Model quantization represents model weights, activations, or cache values with fewer bits to reduce memory traffic, storage, energy, and often inference latency. This guide explains the mechanism, ...

Fine-tuning large language models (LLMs) might sound like a task reserved for tech wizards with endless resources, but the reality is far more approachable—and surprisingly exciting. If you’ve ever ...

Researchers have demonstrated a way to run a 70-billion-parameter language model across four consumer home devices while ...

Researchers from Skoltech and Sberbank's Center for Practical Artificial Intelligence have proposed a new method, TOHA, for detecting hallucinations in large language models operating in ...

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models. Two years ago, Yuri Burda and Harri ...