license:cc-by-4.0 region:us
Līla is a comprehensive benchmark for mathematical reasoning with over 140K natural language questions annotated with Python programs and natural language instructions. The data set comes with multiple splits: Līla-IID (train, dev, test), Līla-OOD (train, dev, test), and Līla-Robust.
@INPROCEEDINGS{Mishra2022Lila,
  author = {
    Swaroop Mishra 
      and Matthew Finlayson 
      and Pan Lu 
      and Leonard Tang 
      and Sean Welleck 
      and Chitta Baral 
      and Tanmay Rajpurohit 
      and Oyvind Tafjord 
      and Ashish Sabharwal 
      and Peter Clark 
      and Ashwin Kalyan},
  title = {Lila: A Unified Benchmark for Mathematical Reasoning},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2022}
}
---
license: cc-by-4.0
---

## Dataset Description

- **Repository:** [allenai/lila](https://github.com/allenai/lila)
- **Paper:** [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392.pdf)
- **Point of Contact:** [Matthew Finlayson](https://mattf1n.github.io/), [Sean Welleck](https://wellecks.com/)

# Lila: A Unified Benchmark for Mathematical Reasoning


- **Homepage: https://lila.apps.allenai.org/** 
- **Repository: https://github.com/allenai/lila** 
- **Paper: https://aclanthology.org/2022.emnlp-main.392.pdf** 


### Licensing Information

Creative Commons Attribution 4.0 International

### Citation Information

Cite this dataset and the source datasets (see [sources.bib](https://github.com/allenai/Lila/blob/main/sources.bib)). 

```bib
@INPROCEEDINGS{Mishra2022Lila,
  author = {
    Swaroop Mishra 
      and Matthew Finlayson 
      and Pan Lu 
      and Leonard Tang 
      and Sean Welleck 
      and Chitta Baral 
      and Tanmay Rajpurohit 
      and Oyvind Tafjord 
      and Ashish Sabharwal 
      and Peter Clark 
      and Ashwin Kalyan},
  title = {Lila: A Unified Benchmark for Mathematical Reasoning},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2022}
}
```
