DOSA下载 - DOSA源代码下载

DOSA

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DOSA：印度不同地理亚文化的社会文物数据集

该存储库托管在DOSA数据集上运行实验的代码。

创造环境

通过运行create_env.py创建dosa conda 环境

通过运行conda activate dosa激活环境

环境变量

在 .env 文件中设置以下环境变量

OPENAI_API_KEY
HF_TOKEN

另外，导出PYTHONPATH变量，以便所有包都可以正常工作。要添加PYTHONPATH ，请在终端上编写以下命令： export PYTHONPATH=$PYTHONPATH:

注意确保您申请了 Llama 2 模型的访问权限。另外，我们使用 HuggingFace 下载 llama2 模型。确保您使用的电子邮件 ID 与申请访问 llama 2 模型时使用的电子邮件 ID 相同。生成HF_TOKEN并将其存储在.env文件中

引文

如果您使用数据集或代码，请使用以下 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.",
}