# IndicSQuAD: A Comprehensive Multilingual Question Answering Dataset for Indic Languages

arXiv: https://arxiv.org/abs/2505.03688 · HTML: https://mlsanity.com/paper/2505.03688/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 15 of 15)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022)
- [L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages](https://mlsanity.com/paper/2211.11418/) (2022)
- [L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library](https://mlsanity.com/paper/2205.14728/) (2022)
- [INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages](https://mlsanity.com/paper/2407.13522/) (2024)
- [L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERT](https://mlsanity.com/paper/2304.11434/) (2023)
- [L3Cube-IndicQuest: A Benchmark Question Answering Dataset for Evaluating Knowledge of LLMs in Indic Context](https://mlsanity.com/paper/2409.08706/) (2024)
- [BanglaQuAD: A Bengali Open-domain Question Answering Dataset](https://mlsanity.com/paper/2410.10229/) (2024)
- [mahaNLP: A Marathi Natural Language Processing Library](https://mlsanity.com/paper/2311.02579/) (2023)
- [Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability](https://mlsanity.com/paper/2407.00454/) (2024)
- [MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering](https://mlsanity.com/paper/2404.13364/) (2024)

## Built on by (most cited first, 5 of 5)

- [Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysis](https://mlsanity.com/paper/2508.19831/) (2025)
- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)
- [MATA: Mindful Assessment of the Telugu Abilities of Large Language Models](https://mlsanity.com/paper/2508.13526/) (2025)
- [LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answering](https://mlsanity.com/paper/2601.03025/) (2026)
- [L3Cube-IndicQuest v2: A Large-Scale Multilingual Benchmark for Evaluating Factual Knowledge of Large Language Models Across Indic Languages](https://mlsanity.com/paper/2608.15535/) (2026)

## Abstract (arXiv)

The rapid progress in question-answering (QA) systems has predominantly benefited high-resource languages, leaving Indic languages largely underrepresented despite their vast native speaker base. In this paper, we present IndicSQuAD, a comprehensive multi-lingual extractive QA dataset covering nine major Indic languages, systematically derived from the SQuAD dataset. Building on previous work with MahaSQuAD for Marathi, our approach adapts and extends translation techniques to maintain high linguistic fidelity and accurate answer-span alignment across diverse languages. IndicSQuAD comprises extensive training, validation, and test sets for each language, providing a robust foundation for model development. We evaluate baseline performances using language-specific monolingual BERT models and the multilingual MuRIL-BERT. The results indicate some challenges inherent in low-resource settings. Moreover, our experiments suggest potential directions for future work, including expanding to additional languages, developing domain-specific datasets, and incorporating multimodal data. The dataset and models are publicly shared at https://github.com/l3cube-pune/indic-nlp