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Found 3 papers, 2 papers with code
RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models models via Romanization
This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages using non-Roman scripts.
IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages
Prior to this work, there was (i) no parallel training data spanning all 22 languages, (ii) no robust benchmarks covering all these languages and containing content relevant to India, and (iii) no existing translation models which support all the 22 scheduled languages of India.
CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation
We learn a regression model, CTQ Scorer (Contextual Translation Quality), that selects examples based on multiple features in order to maximize the translation quality.
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