region:us
Logical reasoning is an important ability to examine, analyze, and critically evaluate arguments as they occur in ordinary
language as the definition from LSAC. ReClor is a dataset extracted from logical reasoning questions of standardized graduate
admission examinations. Empirical results show that the state-of-the-art models struggle on ReClor with poor performance
indicating more research is needed to essentially enhance the logical reasoning ability of current models. We hope this
dataset could help push Machine Reading Comprehension (MRC) towards more complicated reasonin
@inproceedings{yu2020reclor,
        author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
        title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
        booktitle = {International Conference on Learning Representations (ICLR)},
        month = {April},
        year = {2020}
    }
---
paperswithcode_id: reclor
pretty_name: ReClor
dataset_info:
  features:
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: answers
    sequence: string
  - name: label
    dtype: string
  - name: id_string
    dtype: string
  splits:
  - name: train
    num_bytes: 4711114
    num_examples: 4638
  - name: test
    num_bytes: 1017354
    num_examples: 1000
  - name: validation
    num_bytes: 518604
    num_examples: 500
  download_size: 0
  dataset_size: 6247072
---

### Contributions

Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@JetRunner](https://github.com/JetRunner), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lhoestq](https://github.com/lhoestq) for adding this dataset.