The Reformer - Pushing the limits of language modeling

Understanding the capabilities, limitations, and societal impact of large language models

Aligning language models to follow instructions

Scaling laws for neural language models

How to train a new language model from scratch using Transformers and Tokenizers

Red-Teaming Large Language Models

Very Large Language Models and How to Evaluate Them

Large Language Models: A New Moore's Law?

Accelerating Vision-Language Models: BridgeTower on Habana Gaudi2

Language Models for Portuguese: A Systematic Mapping Study

The paper surveys language models created for Portuguese, noting that while rapid progress has been made in NLP, development has been uneven across languages. It systematically maps 46 Portuguese models, detailing aspects such as base model, architecture, resources, datasets, licensing, code, data, and weights. The study also traces model evolution phylogenetically, highlights research gaps, and outlines future directions for Portuguese language modeling.

Transmuting prompts into weights

arXiv:2510. 08734v3 Announce Type: replace Abstract: A growing body of research has demonstrated that the behavior of large language models can be effectively controlled at inference time by directly modifying their internal states, either through vector additions to their activations or through updates to their weight matrices.

Efficient training of language models to fill in the middle

A Dive into Vision-Language Models