region:us
---
dataset_info:
  features:
  - name: bit
    dtype: string
  - name: flip
    dtype: string
  - name: title
    dtype: string
  - name: categories
    dtype: string
  - name: abstract
    dtype: string
  - name: authors
    dtype: string
  - name: doi
    dtype: string
  - name: id
    dtype: string
  splits:
  - name: train
    num_bytes: 229044314
    num_examples: 100039
  download_size: 127335112
  dataset_size: 229044314
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Dataset Card for "arxiv-bit-flip-cs.LG"

This dataset contains "Bit-Flips," structured representations extracted from the abstracts of ArXiv papers, specifically in the category of `cs.LG` (Machine Learning). These Bit-Flips aim to encapsulate the essence of the research by identifying the conventional belief or 'status quo' the abstract challenges (Bit) and the counterargument or innovative approach that flips the Bit (Flip).

## Bit-Flip Concept
A Bit-Flip serves as a two-part schema:

* _Bit_: It identifies the conventional belief or assumption that the research implicitly or explicitly challenges. It is composed of three sentences that are logically connected.
* _Flip_: It formulates the counterargument or innovative approach that flips the conventional belief or Bit. It also consists of three logically connected sentences.

## Data Collection
The dataset focuses on the ArXiv category of `cs.LG` (Machine Learning). The dataset was created to understand the paradigm shifts or challenges to conventional wisdom that are presented in new research, encapsulated through the Bit-Flip schema.

## Methodology
The data was processed using a Python script that performs the following steps:

1. The script generates a custom prompt based on each abstract, using a predefined template that explains the Bit-Flip concept.
2. An Azure model is used to generate a response to the custom prompt.
3. The response is parsed to extract a JSON-like structure containing the Bit and the Flip.
4. Each Bit and Flip is saved along with the title of the paper.
5. The script uses multithreading to speed up the data processing and can handle a batch of abstracts in each run. The processed data is saved in a CSV file.