# IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages

arXiv: https://arxiv.org/abs/2305.16307 · HTML: https://mlsanity.com/paper/2305.16307/

## Key facts

- Cited by 152 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2023.

## Built on by (most cited first, 30 of 152)

- [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://mlsanity.com/paper/2309.04662/) (2023)
- [MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks](https://mlsanity.com/paper/2311.07463/) (2023)
- [IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages](https://mlsanity.com/paper/2404.16816/) (2024)
- [IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages](https://mlsanity.com/paper/2403.06350/) (2024)
- [PARIKSHA: A Large-Scale Investigation of Human-LLM Evaluator Agreement on Multilingual and Multi-Cultural Data](https://mlsanity.com/paper/2406.15053/) (2024)
- [Airavata: Introducing Hindi Instruction-tuned LLM](https://mlsanity.com/paper/2401.15006/) (2024)
- [RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization](https://mlsanity.com/paper/2401.14280/) (2024)
- [MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment](https://mlsanity.com/paper/2410.05873/) (2024)
- [INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages](https://mlsanity.com/paper/2407.13522/) (2024)
- [Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus](https://mlsanity.com/paper/2410.14815/) (2024)
- [An Empirical Comparison of Vocabulary Expansion and Initialization Approaches for Language Models](https://mlsanity.com/paper/2407.05841/) (2024)
- [Multilingual Large Language Models: A Systematic Survey](https://mlsanity.com/paper/2411.11072/) (2024)
- [GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages](https://mlsanity.com/paper/2410.23825/) (2024)
- [Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs](https://mlsanity.com/paper/2405.20835/) (2024)
- [Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code](https://mlsanity.com/paper/2404.00399/) (2024)
- [Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning](https://mlsanity.com/paper/2405.19462/) (2024)
- [Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter](https://mlsanity.com/paper/2501.14491/) (2025)
- [Krutrim LLM: Multilingual Foundational Model for over a Billion People](https://mlsanity.com/paper/2502.09642/) (2025)
- [Chitrarth: Bridging Vision and Language for a Billion People](https://mlsanity.com/paper/2502.15392/) (2025)
- [Llama-3-Nanda-10B-Chat: An Open Generative Large Language Model for Hindi](https://mlsanity.com/paper/2504.06011/) (2025)
- [An Empirical Study of In-context Learning in LLMs for Machine Translation](https://mlsanity.com/paper/2401.12097/) (2024)
- [TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking](https://mlsanity.com/paper/2502.11187/) (2025)
- [Assessing Translation capabilities of Large Language Models involving English and Indian Languages](https://mlsanity.com/paper/2311.09216/) (2023)
- [Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs](https://mlsanity.com/paper/2403.05434/) (2024)
- [Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings](https://mlsanity.com/paper/2407.14056/) (2024)
- [Table Question Answering for Low-resourced Indic Languages](https://mlsanity.com/paper/2410.03576/) (2024)
- [MorphTok: Morphologically Grounded Tokenization for Indian Languages](https://mlsanity.com/paper/2504.10335/) (2025)
- [Pretraining Language Models Using Translationese](https://mlsanity.com/paper/2403.13638/) (2024)
- [How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages?](https://mlsanity.com/paper/2406.03893/) (2024)
- [Chitranuvad: Adapting Multi-Lingual LLMs for Multimodal Translation](https://mlsanity.com/paper/2502.20420/) (2025)

## Abstract (arXiv)

India has a rich linguistic landscape with languages from 4 major language families spoken by over a billion people. 22 of these languages are listed in the Constitution of India (referred to as scheduled languages) are the focus of this work. Given the linguistic diversity, high-quality and accessible Machine Translation (MT) systems are essential in a country like India. 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. In this work, we aim to address this gap by focusing on the missing pieces required for enabling wide, easy, and open access to good machine translation systems for all 22 scheduled Indian languages. We identify four key areas of improvement: curating and creating larger training datasets, creating diverse and high-quality benchmarks, training multilingual models, and releasing models with open access. Our first contribution is the release of the Bharat Parallel Corpus Collection (BPCC), the largest publicly available parallel corpora for Indic languages. BPCC contains a total of 230M bitext pairs, of which a total of 126M were newly added, including 644K manually translated sentence pairs created as part of this work. Our second contribution is the release of the first n-way parallel benchmark covering all 22 Indian languages, featuring diverse domains, Indian-origin content, and source-original test sets. Next, we present IndicTrans2, the first model to support all 22 languages, surpassing existing models on multiple existing and new benchmarks created as a part of this work. Lastly, to promote accessibility and collaboration, we release our models and associated data with permissive licenses at https://github.com/AI4Bharat/IndicTrans2.