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---
license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- hi
tags:
- chatrag
- hindi
pretty_name: Hindi ChatRAG
size_categories:
- 10K<n<100K
configs:
- config_name: inscit
data_files:
- split: test
path: data/inscit/test.json
- config_name: hybridial
data_files:
- split: test
path: data/hybridial/test.json
- config_name: doc2dial
data_files:
- split: test
path: data/doc2dial/test.json
- config_name: quac
data_files:
- split: test
path: data/quac/test.json
- config_name: qrecc
data_files:
- split: test
path: data/qrecc/test.json
- config_name: doqa_cooking
data_files:
- split: test
path: data/doqa_cooking/test.json
- config_name: doqa_movies
data_files:
- split: test
path: data/doqa_movies/test.json
- config_name: doqa_travel
data_files:
- split: test
path: data/doqa_travel/test.json
---
## Dataset Description:
The ChatRAG-Hi (Hindi ChatRAG Bench) dataset is based on the English version of the ChatRAG Bench, which comprises the following ten datasets: Doc2Dial, QuAC, QReCC, INSCIT, HybriDialogue, DoQA, and ConvFinQA. The dataset was translated using GCP, and approximately 500 samples were filtered from each of these sets based on backtranslation accuracy to eliminate poor translations.<br>
The evaluation steps are described [here](https://huggingface.co/datasets/nvidia/ChatRAG-Hi/blob/main/evaluation/README.md).
## Dataset Owner:
NVIDIA Corporation
## Dataset Creation Date:
April 2025
## License/Terms of Use:
This dataset is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
## Intended Usage:
This dataset is used to evaluate a large language model’s (LLM) conversational QA capability over documents or retrieved context in the Hindi Language.
## Dataset Characterization
Data Collection Method<br>
* Synthetic <br>
Labeling Method<br>
* Synthetic <br>
## Dataset Format
Text
## Dataset Quantification
474MB of prompt-response pairs, comprising 5948 individual samples.
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility, and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
## Citing
If you find our work helpful, please consider citing our paper:
```
@article{kamath2025benchmarking,
title={Benchmarking Hindi LLMs: A New Suite of Datasets and a Comparative Analysis},
author={Kamath, Anusha and Singla, Kanishk and Paul, Rakesh and Joshi, Raviraj and Vaidya, Utkarsh and Chauhan, Sanjay Singh and Wartikar, Niranjan},
journal={arXiv preprint arXiv:2508.19831},
year={2025}
}
```
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