  Open source large language models (LLMs) have shown great improvements in
recent times. However, many of these models are focused solely on popular
spoken languages. We present a high quality dataset of more than 70k
prompt-response pairs in 74 languages which consist of human generated prompts
and synthetic responses. We use this dataset to train a state-of-the-art open
source English LLM to chat multilingually. We evaluate our model on MT-Bench
chat benchmarks in 6 languages, finding that our multilingual model outperforms
previous state-of-the-art open source LLMs across each language. We further
find that training on more multilingual data is beneficial to the performance
in a chosen target language (Japanese) compared to simply training on only data
in that language. These results indicate the necessity of training on large
amounts of high quality multilingual data to make a more accessible LLM.
