We introduce MADLAD-400, a manually audited, general domain 3T token monolingual
dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations …

Large language models (LLMs) excel at few-shot in-context learning (ICL)--learning from a
few examples provided in context at inference, without any weight updates. Newly expanded …

Recent breakthroughs in large language models (LLMs) have centered around a handful of
data-rich languages. What does it take to broaden access to breakthroughs beyond first …

We investigate a surprising limitation of LLMs: their inability to consistently generate text in a
user's desired language. We create the Language Confusion Benchmark (LCB) to evaluate …

Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

Temporal reasoning represents a vital component of human communication and
understanding, yet remains an underexplored area within the context of Large Language …

Large language models (LLMs) have garnered significant interest in natural language
processing (NLP), particularly their remarkable performance in various downstream tasks in …

Multilingual large language models (MLLMs) leverage advanced large language models to
process and respond to queries across multiple languages, achieving significant success in …

As model context lengths continue to increase, the number of demonstrations that can be
provided in-context approaches the size of entire training datasets. We study the behavior of …

The recent development and success of Large Language Models (LLMs) necessitate an
evaluation of their performance across diverse NLP tasks in different languages. Although …