Transformers

Improving a large language model’s factual accuracy typically requires making it bigger, which in turn, involves more computation. Researchers devised an architecture that enables models to recall relevant details without significantly increasing the amount of computation required.

Large language models can improve systems that recommend items to purchase by inferring customer preferences.

Researchers built a model that’s more robust to noisy inputs like misspellings, smarter about character-level information like the number of R's in strawberry, and potentially better able to understand unfamiliar languages that might share groups of letters with familiar languages.

If you have a collection of variables that represent, say, a cancer patient and you want to classify the patient’s illness as likely cancer or not, algorithms based on decision trees, such as gradient-boosted trees, typically perform better than neural networks.

Although large language models can improve their performance by generating a chain of thought (CoT) — intermediate text tokens that break down the process of responding to a prompt into a series of steps.

Contrastive loss functions make it possible to produce good embeddings without labeled data. A twist on this idea makes even more useful embeddings.

Google’s Gemini 2.0 Flash, the first member of its updated Gemini family of large multimodal models, combines speed with performance that exceeds that of its earlier flagship model, Gemini 1.5 Pro, on several measures.

An open source model is designed to perform sophisticated object detection on edge devices like phones, cars, medical equipment, and smart doorbells.

Researchers cut the processing required to train transformers by around 20 percent with only a slight degradation in performance.

Researchers have probed the inner workings of individual layers of large language models. A new tool applies this approach to all layers.

A new model generates tokens faster than current transformers, especially when processing long inputs.

A man who lost the ability to speak four years ago is sounding like his earlier self, thanks to a collection of brain implants and machine learning models.