# L3Cube-IndicQuest: A Benchmark Question Answering Dataset for Evaluating Knowledge of LLMs in Indic Context

arXiv: https://arxiv.org/abs/2409.08706 · HTML: https://mlsanity.com/paper/2409.08706/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [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)
- [Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages](https://mlsanity.com/paper/2212.05409/) (2022)
- [A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias](https://mlsanity.com/paper/2404.00929/) (2024)
- [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)

## Built on by (most cited first, 10 of 10)

- [Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus](https://mlsanity.com/paper/2410.14815/) (2024)
- [DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture](https://mlsanity.com/paper/2509.19274/) (2025)
- [IndicSQuAD: A Comprehensive Multilingual Question Answering Dataset for Indic Languages](https://mlsanity.com/paper/2505.03688/) (2025)
- [Pragyaan: Designing and Curating High-Quality Cultural Post-Training Datasets for Indian Languages](https://mlsanity.com/paper/2510.07000/) (2025)
- [Common to Whom? Regional Cultural Commonsense and LLM Bias in India](https://mlsanity.com/paper/2601.15550/) (2026)
- [Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysis](https://mlsanity.com/paper/2508.19831/) (2025)
- [Better To Ask in English? Evaluating Factual Accuracy of Multilingual LLMs in English and Low-Resource Languages](https://mlsanity.com/paper/2504.20022/) (2025)
- [Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain](https://mlsanity.com/paper/2507.16974/) (2025)
- [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)
- [V=akQA: A Benchmark and Evaluation Study for Telugu Spoken Factoid Question Answering](https://mlsanity.com/paper/2609.19879/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have made significant progress in incorporating Indic languages within multilingual models. However, it is crucial to quantitatively assess whether these languages perform comparably to globally dominant ones, such as English. Currently, there is a lack of benchmark datasets specifically designed to evaluate the regional knowledge of LLMs in various Indic languages. In this paper, we present the L3Cube-IndicQuest, a gold-standard factual question-answering benchmark dataset designed to evaluate how well multilingual LLMs capture regional knowledge across various Indic languages. The dataset contains 200 question-answer pairs, each for English and 19 Indic languages, covering five domains specific to the Indic region. We aim for this dataset to serve as a benchmark, providing ground truth for evaluating the performance of LLMs in understanding and representing knowledge relevant to the Indian context. The IndicQuest can be used for both reference-based evaluation and LLM-as-a-judge evaluation. The dataset is shared publicly at https://github.com/l3cube-pune/indic-nlp .