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 …

This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

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 …

In this paper, we present a new embedding model, called M3-Embedding, which is
distinguished for its versatility in Multi-Linguality, Multi-Functionality, and Multi-Granularity. It …

Although large language models (LLMs) demonstrate impressive performance for many
language tasks, most of them can only handle texts a few thousand tokens long, limiting their …

Large language models (LLMs) have gained widespread adoption in various natural
language processing tasks, including question answering and dialogue systems. However …

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 …

Pre-trained models have achieved state-of-the-art results in various Natural Language
Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up …

Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Confidently making progress on multilingual modeling requires challenging, trustworthy
evaluations. We present TyDi QA—a question answering dataset covering 11 typologically …