# Airavata: Introducing Hindi Instruction-tuned LLM

arXiv: https://arxiv.org/abs/2401.15006 · HTML: https://mlsanity.com/paper/2401.15006/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 38 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [MEGA: Multilingual Evaluation of Generative AI](https://mlsanity.com/paper/2303.12528/) (2023)
- [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://mlsanity.com/paper/2212.12017/) (2022)
- [IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages](https://mlsanity.com/paper/2305.16307/) (2023)
- [Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages](https://mlsanity.com/paper/2212.05409/) (2022)
- [Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation](https://mlsanity.com/paper/2305.15011/) (2023)
- [PolyLM: An Open Source Polyglot Large Language Model](https://mlsanity.com/paper/2307.06018/) (2023)
- [IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://mlsanity.com/paper/2203.05437/) (2022)
- [An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models](https://mlsanity.com/paper/2301.09211/) (2023)
- [HateCheckHIn: Evaluating Hindi Hate Speech Detection Models](https://mlsanity.com/paper/2205.00328/) (2022)

## Built on by (most cited first, 30 of 32)

- [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)
- [TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs](https://mlsanity.com/paper/2403.00393/) (2024)
- [INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages](https://mlsanity.com/paper/2407.13522/) (2024)
- [NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities](https://mlsanity.com/paper/2505.18383/) (2025)
- [One Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual Tokenizers](https://mlsanity.com/paper/2506.10766/) (2025)
- [SUTRA: Scalable Multilingual Language Model Architecture](https://mlsanity.com/paper/2405.06694/) (2024)
- [Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code](https://mlsanity.com/paper/2404.00399/) (2024)
- [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)
- [Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia](https://mlsanity.com/paper/2503.07920/) (2025)
- [A Three-Pronged Approach to Cross-Lingual Adaptation with Multilingual LLMs](https://mlsanity.com/paper/2406.17377/) (2024)
- [Chitranuvad: Adapting Multi-Lingual LLMs for Multimodal Translation](https://mlsanity.com/paper/2502.20420/) (2025)
- [Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages](https://mlsanity.com/paper/2401.18034/) (2024)
- [Multilingual Mathematical Reasoning: Advancing Open-Source LLMs in Hindi and English](https://mlsanity.com/paper/2412.18415/) (2024)
- [Chandomitra: Towards Generating Structured Sanskrit Poetry from Natural Language Inputs](https://mlsanity.com/paper/2506.00815/) (2025)
- [Multilingual Tokenization through the Lens of Indian Languages: Challenges and Insights](https://mlsanity.com/paper/2506.17789/) (2025)
- [AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models](https://mlsanity.com/paper/2509.02133/) (2025)
- [BhashaKritika: Building Synthetic Pretraining Data at Scale for Indic Languages](https://mlsanity.com/paper/2511.10338/) (2025)
- [BrahmicTokenizer-131K: An Indic-Capable Drop-In Replacement for o200k_base](https://mlsanity.com/paper/2605.29379/) (2026)
- [Suvach -- Generated Hindi QA benchmark](https://mlsanity.com/paper/2404.19254/) (2024)
- [Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction](https://mlsanity.com/paper/2501.15219/) (2025)
- [UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings](https://mlsanity.com/paper/2502.16961/) (2025)
- [Buy versus Build an LLM: A Decision Framework for Governments](https://mlsanity.com/paper/2602.13033/) (2026)
- [SUKHSANDESH: An Avatar Therapeutic Question Answering Platform for Sexual Education in Rural India](https://mlsanity.com/paper/2405.01858/) (2024)
- [Multilingual Non-Factoid Question Answering with Answer Paragraph Selection](https://mlsanity.com/paper/2408.10604/) (2024)
- [IndRegBias: A Dataset for Studying Indian Regional Biases in English and Code-Mixed Social Media Comments](https://mlsanity.com/paper/2601.06477/) (2026)
- [Chitrakshara: A Large Multilingual Multimodal Dataset for Indian languages](https://mlsanity.com/paper/2603.23521/) (2026)

## Abstract (arXiv)

We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we also share the IndicInstruct dataset, which is a collection of diverse instruction-tuning datasets to enable further research for Indic LLMs. Additionally, we present evaluation benchmarks and a framework for assessing LLM performance across tasks in Hindi. Currently, Airavata supports Hindi, but we plan to expand this to all 22 scheduled Indic languages. You can access all artifacts at https://ai4bharat.github.io/airavata.