Deploy Multilingual LLMs with NVIDIA NIM

Originally published at: https://developer.nvidia.com/blog/deploy-multilingual-llms-with-nvidia-nim/

Multilingual large language models (LLMs) are increasingly important for enterprises operating in today’s globalized business landscape. As businesses expand their reach across borders and cultures, the ability to communicate effectively in multiple languages is crucial for success. By supporting and investing in multilingual LLMs, enterprises can break down language barriers, foster inclusivity, and gain a…

Hi team, this is fantastic work. However, I have a few questions regarding the Hindi example you’ve given.

There’s a massive difference in how Hindi is read and how it’s spoken in everyday conversations. For instance, real users are never going to ask an LLM a question in pure Hindi, like you have cited in your example. In India, spoken Hindi is a mix of everyday Hindi (which is wildly different to bookish Hindi, which is probably what the models are trained on) and colloquial English terms. Here’s an example of a real use-case:

“मैं अपने time management skills को कैसे सुधार सकता हूँ? मुझे पांच points बताएं और उनका elaboration करें।”

This is a more realistic input, which is likely to be from a voice instance, as opposed to someone typing it out on a keyboard. Similarly, the LLMs output must also adapt to the “style” of Hindi, given the dilution of the alphabet through use of “plugged-in” English phrases. This scenario is likely to be true not just for Hindi but many other vernacular scenarios.

Further, there remain significant challenges with regards to transcribing spoken Hindi accurately, understanding the significant difference between how its spoken and how it’s written in text.

What would be a recommended approach to overcoming these challenges?

Thanks you for the read, really interesting potential! I have two small edits:

The comma after 64 should not be there

url needs a c ;)

Great point, and indeed, this issue comes up with many languages, especially those with multiple dialects. Multi-language LLMs are typically trained in a mixed language strategy (i.e. Hindi+English) in order to comprehend input with multiple languages (It’s very common to refer to technical terms in English). As for output, the solution to better replicate the language is by instruction tuning with additional data samples. Please see this blog for additional details: https://developer.nvidia.com/blog/regional-llms-sea-lion-and-seallm-serve-languages-and-cultures-of-southeast-asia/