configs:
- config_name: default
data_files:
- split: pubmed
path: data/pubmed-*
- split: nonbiomedical
path: data/nonbiomedical-*
- split: counterfactual
path: data/counterfactual-*
- split: casual
path: data/casual-*
- split: rap
path: data/rap-*
dataset_info:
features:
- name: PubmedData
struct:
- name: ArticleIdList
sequence:
- name: ArticleId
sequence: string
- name: PublicationStatus
dtype: string
- name: History
struct:
- name: PubMedPubDate
sequence:
- name: Year
dtype: int32
- name: Month
dtype: int32
- name: Day
dtype: int32
- name: ReferenceList
sequence:
- name: Citation
dtype: string
- name: CitationId
dtype: int32
- name: text
dtype: string
splits:
- name: pubmed
num_bytes: 1166668
num_examples: 1000
- name: nonbiomedical
num_bytes: 1141909
num_examples: 1000
- name: counterfactual
num_bytes: 1179347
num_examples: 991
- name: casual
num_bytes: 1205949
num_examples: 1000
- name: rap
num_bytes: 1252260
num_examples: 1000
download_size: 3357032
dataset_size: 5946133
language:
- en
A corpus of rewritten pubmed abstracts
This corpus contains a 1k example subset from the pubmed corpus and various rewritten versions. The rewritten versions change one aspect of the orginal text and keeps other aspects unchanged as much as possible.
Another corpus of rewritten general text is provided here: c4_derived
Data Splits
pubmed: a 1k example subset from the original pubmed corpus
nonbiomedical: main topic of text changed to nonbiomedical topic
counerfactual: factuals knowledge in text replaced by incorrect factuals
casual: style of text changed to a casual style
rap: style of text changed to a rap style
Dataset Creation
Text is generated by ChatGPT with corresponding prompts. Refer to the paper for the instructions used to generate text in each derived subsets.
Please check the terms and conditions of pubmed data here.
Citation Information
@inproceedings{
zhang2024dissecting,
title={Dissecting learning and forgetting in language model finetuning},
author={Xiao Zhang and Ji Wu},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=tmsqb6WpLz}
}