Assessing Translation capabilities of Large Language Models involving
  English and Indian Languages
Vandan Mujadia Ashok Urlana Yash Bhaskar Penumalla Aditya Pavani Kukkapalli Shravya Parameswari Krishnamurthy Dipti Misra Sharma
Generative Large Language Models (LLMs) have achieved remarkable advancements
in various NLP tasks. In this work, our aim is to explore the multilingual
capabilities of large language models by using machine translation as a task
involving English and 22 Indian languages. We first investigate the translation
capabilities of raw large language models, followed by exploring the in-context
learning capabilities of the same raw models. We fine-tune these large language
models using parameter efficient fine-tuning methods such as LoRA and
additionally with full fine-tuning. Through our study, we have identified the
best performing large language model for the translation task involving LLMs,
which is based on LLaMA.
  Our results demonstrate significant progress, with average BLEU scores of
13.42, 15.93, 12.13, 12.30, and 12.07, as well as CHRF scores of 43.98, 46.99,
42.55, 42.42, and 45.39, respectively, using 2-stage fine-tuned LLaMA-13b for
English to Indian languages on IN22 (conversational), IN22 (general),
flores200-dev, flores200-devtest, and newstest2019 testsets. Similarly, for
Indian languages to English, we achieved average BLEU scores of 14.03, 16.65,
16.17, 15.35 and 12.55 along with chrF scores of 36.71, 40.44, 40.26, 39.51,
and 36.20, respectively, using fine-tuned LLaMA-13b on IN22 (conversational),
IN22 (general), flores200-dev, flores200-devtest, and newstest2019 testsets.
Overall, our findings highlight the potential and strength of large language
models for machine translation capabilities, including for languages that are
currently underrepresented in LLMs.