# IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages

arXiv: https://arxiv.org/abs/2403.06350 · HTML: https://mlsanity.com/paper/2403.06350/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [SemDeDup: Data-efficient learning at web-scale through semantic deduplication](https://mlsanity.com/paper/2303.09540/) (2023)
- [Scaling Laws and Interpretability of Learning from Repeated Data](https://mlsanity.com/paper/2205.10487/) (2022)
- [IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages](https://mlsanity.com/paper/2305.16307/) (2023)
- [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://mlsanity.com/paper/2201.06642/) (2022)
- [Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages](https://mlsanity.com/paper/2212.05409/) (2022)
- [Airavata: Introducing Hindi Instruction-tuned LLM](https://mlsanity.com/paper/2401.15006/) (2024)
- [Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users](https://mlsanity.com/paper/2205.03018/) (2022)
- [A Multilingual Parallel Corpora Collection Effort for Indian Languages](https://mlsanity.com/paper/2007.07691/) (2020)
- [Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages](https://mlsanity.com/paper/2305.15814/) (2023)
- [indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languages](https://mlsanity.com/paper/2203.16825/) (2022)

## Built on by (most cited first, 30 of 52)

- [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)
- [Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models](https://mlsanity.com/paper/2410.01335/) (2024)
- [An Empirical Comparison of Vocabulary Expansion and Initialization Approaches for Language Models](https://mlsanity.com/paper/2407.05841/) (2024)
- [One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks](https://mlsanity.com/paper/2409.13920/) (2024)
- [Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings](https://mlsanity.com/paper/2410.13153/) (2024)
- [Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation](https://mlsanity.com/paper/2408.12780/) (2024)
- [One Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual Tokenizers](https://mlsanity.com/paper/2506.10766/) (2025)
- [SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2505.10740/) (2025)
- [Krutrim LLM: Multilingual Foundational Model for over a Billion People](https://mlsanity.com/paper/2502.09642/) (2025)
- [The Multilingual Divide and Its Impact on Global AI Safety](https://mlsanity.com/paper/2505.21344/) (2025)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking](https://mlsanity.com/paper/2502.11187/) (2025)
- [Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia](https://mlsanity.com/paper/2503.07920/) (2025)
- [Script Fragmentation and Format: What Drives the English-Bengali Performance Gap in Open LLMs?](https://mlsanity.com/paper/2507.23248/) (2025)
- [Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM](https://mlsanity.com/paper/2508.05775/) (2025)
- [Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings](https://mlsanity.com/paper/2407.14056/) (2024)
- [MorphTok: Morphologically Grounded Tokenization for Indian Languages](https://mlsanity.com/paper/2504.10335/) (2025)
- [PARAM-1 BharatGen 2.9B Model](https://mlsanity.com/paper/2507.13390/) (2025)
- [Bhaasha, Bhasa, Zaban: A Survey for Low-Resourced Languages in South Asia -- Current Stage and Challenges](https://mlsanity.com/paper/2509.11570/) (2025)
- [SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods](https://mlsanity.com/paper/2505.23714/) (2025)
- [UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages](https://mlsanity.com/paper/2509.21294/) (2025)
- [MUTANT: A Recipe for Multilingual Tokenizer Design](https://mlsanity.com/paper/2511.03237/) (2025)
- [Multilingual State Space Models for Structured Question Answering in Indic Languages](https://mlsanity.com/paper/2502.01673/) (2025)
- [UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages](https://mlsanity.com/paper/2503.23088/) (2025)
- [Pragyaan: Designing and Curating High-Quality Cultural Post-Training Datasets for Indian Languages](https://mlsanity.com/paper/2510.07000/) (2025)
- ["UberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset](https://mlsanity.com/paper/2602.15210/) (2026)
- [Enhancing Out-of-Vocabulary Performance of Indian TTS Systems for Practical Applications through Low-Effort Data Strategies](https://mlsanity.com/paper/2407.13435/) (2024)
- [Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5](https://mlsanity.com/paper/2409.05401/) (2024)
- [IndicSentEval: How Effectively do Multilingual Transformer Models encode Linguistic Properties for Indic Languages?](https://mlsanity.com/paper/2410.02611/) (2024)

## Abstract (arXiv)

Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. Our work aims to bridge this divide by introducing an expansive suite of resources specifically designed for the development of Indic LLMs, covering 22 languages, containing a total of 251B tokens and 74.8M instruction-response pairs. Recognizing the importance of both data quality and quantity, our approach combines highly curated manually verified data, unverified yet valuable data, and synthetic data. We build a clean, open-source pipeline for curating pre-training data from diverse sources, including websites, PDFs, and videos, incorporating best practices for crawling, cleaning, flagging, and deduplication. For instruction-fine tuning, we amalgamate existing Indic datasets, translate/transliterate English datasets into Indian languages, and utilize LLaMa2 and Mixtral models to create conversations grounded in articles from Indian Wikipedia and Wikihow. Additionally, we address toxicity alignment by generating toxic prompts for multiple scenarios and then generate non-toxic responses by feeding these toxic prompts to an aligned LLaMa2 model. We hope that the datasets, tools, and resources released as a part of this work will not only propel the research and development of Indic LLMs but also establish an open-source blueprint for extending such efforts to other languages. The data and other artifacts created as part of this work are released with permissive licenses.