Machine Learning Research

An architectural innovation improves upon transformers — up to 2 billion parameters, at least...

Large language models sometimes generate false statements. New work makes them more likely to produce factual output.

Humanoid robots can play football (known as soccer in the United States) in the real world, thanks to reinforcement learning.

Research aims to help users select large language models that minimize expenses while maintaining quality.

The technique known as reinforcement learning from human feedback fine-tunes large language models to be helpful and avoid generating harmful responses such as suggesting illegal or dangerous activities. An alternative method streamlines this approach and achieves better results.

Machine learning models typically learn language by training on tasks like predicting the next word in a given text. Researchers trained a language model in a less focused, more human-like way.

Large language models are not good at math. Researchers devised a way to make them better. Tiedong Liu and Bryan Kian Hsiang Low at the National University of Singapore proposed a method to fine-tune large language models for arithmetic tasks.

Reinforcement learning from human feedback (RLHF) is widely used to fine-tune pretrained models to deliver outputs that align with human preferences. New work aligns pretrained models without the cumbersome step of reinforcement learning.

The combination of  language models that are equipped for retrieval augmented generation can retrieve text from a database to improve their output. Further work extends this capability to retrieve information from any application that comes with an API. 

Pruning weights from a neural network makes it smaller and faster, but it can take a lot of computation to choose weights that can be removed without degrading the network’s performance.

Researchers devised a way to reduce memory requirements when fine-tuning large language models. Kai Lv and colleagues at Fudan University proposed low memory optimization (LOMO), a modification of stochastic gradient descent that stores less data than other optimizers during fine-tuning.

The longer text-to-image models train, the better their output — but the training is costly. Researchers built a system that produced superior images after far less training.

Most people understand that others’ mental states can differ from their own. For instance, if your friend leaves a smartphone on a table and you privately put it in your pocket, you understand that your friend continues to believe it was on the table.

Text-to-video has struggled to produce consistent motions like walking and rotation. A new approach achieves more realistic motion.

Machine learning algorithms often struggle with geometry. A language model learned to prove relatively difficult theorems.