  Large Language Models (LLMs) exhibit impressive zero/few-shot inference and
generation quality for high-resource languages (HRLs). A few of them have been
trained on low-resource languages (LRLs) and give decent performance. Owing to
the prohibitive costs of training LLMs, they are usually used as a network
service, with the client charged by the count of input and output tokens. The
number of tokens strongly depends on the script and language, as well as the
LLM's subword vocabulary. We show that LRLs are at a pricing disadvantage,
because the well-known LLMs produce more tokens for LRLs than HRLs. This is
because most currently popular LLMs are optimized for HRL vocabularies. Our
objective is to level the playing field: reduce the cost of processing LRLs in
contemporary LLMs while ensuring that predictive and generative qualities are
not compromised. As means to reduce the number of tokens processed by the LLM,
we consider code-mixing, translation, and transliteration of LRLs to HRLs. We
perform an extensive study using the IndicXTREME classification and six
generative tasks dataset, covering 15 Indic and 3 other languages, while using
GPT-4 (one of the costliest LLM services released so far) as a commercial LLM.
We observe and analyze interesting patterns involving token count, cost, and
quality across a multitude of languages and tasks. We show that choosing the
best policy to interact with the LLM can reduce cost by 90% while giving better
or comparable performance compared to communicating with the LLM in the
original LRL.
