DOSA下載 - DOSA原始碼下載

DOSA

其他源碼

下載

DOSA：印度不同地理次文化的社會文物資料集

此儲存庫託管在DOSA資料集上執行實驗的程式碼。

創造環境

透過運行create_env.py建立dosa conda 環境

透過運行conda activate dosa激活環境

環境變數

在 .env 檔案中設定以下環境變量

OPENAI_API_KEY
HF_TOKEN

另外，匯出PYTHONPATH變量，以便所有套件都可以正常工作。若要新增PYTHONPATH ，請在終端機上編寫以下命令： export PYTHONPATH=$PYTHONPATH:

注意確保您申請了 Llama 2 模型的存取權限。另外，我們使用 HuggingFace 下載 llama2 模型。確保您使用的電子郵件 ID 與申請存取 llama 2 模型時使用的電子郵件 ID 相同。產生HF_TOKEN並將其儲存在.env檔案中

引文

如果您使用資料集或程式碼，請使用以下 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.",
}