Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Transformer-based pretrained language models (T-PTLMs) have achieved great success in
almost every NLP task. The evolution of these models started with GPT and BERT. These …

We introduce C-Pack, a package of resources that significantly advances the field of general
text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a …

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model
with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as …

As language models grow ever larger, the need for large-scale high-quality text datasets has
never been more pressing, especially in multilingual settings. The BigScience workshop, a 1 …

Text-to-Image generation in the general domain has long been an open problem, which
requires both a powerful generative model and cross-modal understanding. We propose …

Prompts for pre-trained language models (PLMs) have shown remarkable performance by
bridging the gap between pre-training tasks and various downstream tasks. Among these …

The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for
Indonesian languages, including opening access to previously non-public resources …

We present the All-Seeing (AS) project: a large-scale data and model for recognizing and
understanding everything in the open world. Using a scalable data engine that incorporates …