README.md

---
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.