DOSA: A Dataset of Social Artifacts from Different Indian Geographical Subcultures

This repo hosts the code to run experiments on the DOSA dataset.

Create Environment

Create the dosa conda environment by running the create_env.py

Activate the environment by running conda activate dosa

Environment Variables

Set the below environment variables in the .env file

• OPENAI_API_KEY
• HF_TOKEN

Also, export the PYTHONPATH variable so that all the packages can work correctly. To add PYTHONPATH, write this command on your terminal: export PYTHONPATH=$PYTHONPATH:<path to cultural_artifacts>

Note Make sure that you apply for an access to Llama 2 model. Also, we use HuggingFace to download the llama2 model. Ensure that you use the same email id as the one that you used to apply for the access to the llama 2 model. Generate the HF_TOKEN and then store it in the .env file

Citation

If you're using the dataset, or the code, then please use the following bibTEX:

@inproceedings{seth-etal-2024-dosa-dataset,
    title = "{DOSA}: A Dataset of Social Artifacts from Different {I}ndian Geographical Subcultures",
    author = "Seth, Agrima  and
      Ahuja, Sanchit  and
      Bali, Kalika  and
      Sitaram, Sunayana",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.474",
    pages = "5323--5337",
    abstract = "Generative models are increasingly being used in various applications, such as text generation, commonsense reasoning, and question-answering. To be effective globally, these models must be aware of and account for local socio-cultural contexts, making it necessary to have benchmarks to evaluate the models for their cultural familiarity. Since the training data for LLMs is web-based and the Web is limited in its representation of information, it does not capture knowledge present within communities that are not on the Web. Thus, these models exacerbate the inequities, semantic misalignment, and stereotypes from the Web. There has been a growing call for community-centered participatory research methods in NLP. In this work, we respond to this call by using participatory research methods to introduce DOSA, the first community-generated Dataset of 615 Social Artifacts, by engaging with 260 participants from 19 different Indian geographic subcultures. We use a gamified framework that relies on collective sensemaking to collect the names and descriptions of these artifacts such that the descriptions semantically align with the shared sensibilities of the individuals from those cultures. Next, we benchmark four popular LLMs and find that they show significant variation across regional sub-cultures in their ability to infer the artifacts.",
}

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