license: mit
task_categories:
- text-classification
- question-answering
- text-generation
language:
- en
pretty_name: e
size_categories:
- 1K<n<10K
Dataset Card for Economics Paper Dataset
Dataset Summary
The Economics Research Paper Dataset was designed to support the development of the LLaMA-2-Econ models, with a focus on Title Generation, Abstract Classification, and Question & Answer (Q&A) tasks. It comprises abstracts and titles of economics research papers, along with synthetic Q&A pairs derived from the abstracts, to facilitate training of large language models for economics-specific applications.
Dataset Description
Content: The dataset includes:
- Economics paper abstracts and titles.
Source: The data was collected using the arXiv API, with papers selected from the categories Econometrics (ec.EM), General Economics (ec.GN), and Theoretical Economics (ec.TH).
Volume:
- Total abstracts and titles: 6362
Intended Uses
This dataset is intended for training and evaluating language models specialized in:
- Generating titles for economics research papers.
- Classifying abstracts into sub-fields of economics.
- Answering questions based on economics paper abstracts.
Dataset Creation
Curation Rationale
The dataset was curated to address the lack of specialized tools and datasets for enhancing research within the economics domain, leveraging the potential of language models like LLaMA-2.
Source Data
Initial Data Collection and Normalization
Data was collected through the arXiv API, targeting papers within specified categories of economics. Titles and abstracts were extracted, and synthetic Q&A pairs were generated using a process that involved the GPT-3.5 Turbo model for contextual dialogue creation.
Licensing Information
The dataset is derived from arXiv papers. Users are advised to adhere to arXiv's terms of use.