---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: CanItEdit
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
tags:
- code-generation
- code
paperswithcode_id: canitedit
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: name
    dtype: string
  - name: full_name
    dtype: string
  - name: before
    dtype: string
  - name: after
    dtype: string
  - name: tests
    dtype: string
  - name: instruction_descriptive
    dtype: string
  - name: instruction_humane
    dtype: string
  - name: taxonomy
    struct:
    - name: change_kind
      dtype: string
    - name: libraries
      sequence: string
    - name: topic
      dtype: string
  splits:
  - name: test
    num_bytes: 301982
    num_examples: 54
  download_size: 136181
  dataset_size: 301982
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---
# Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions
CanItEdit is a benchmark for evaluating LLMs on instructional code editing, the task of updating a program given a natural language instruction. The benchmark contains 54 hand-crafted Python programs with before and after code blocks, two types of natural language instructions (descriptive and lazy), and a hidden test suite.

The dataset’s dual natural language instructions test model efficiency in two scenarios: 
1) Descriptive: Detailed instructions replicate situations where users provide specific specifications or
another model outlines a plan, similar to Reflexion prompting,
2) Lazy: Informal instructions resemble typical user queries
for LLMs in code generation.

For more information and results see [our paper](https://arxiv.org/abs/2312.12450).

## Citation
If you use our work, please cite our paper as such:
```
@misc{cassano2023edit,
      title={Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions}, 
      author={Federico Cassano and Luisa Li and Akul Sethi and Noah Shinn and Abby Brennan-Jones and Anton Lozhkov and Carolyn Jane Anderson and Arjun Guha},
      year={2023},
      eprint={2312.12450},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}
```

## How To Evaluate
All the code for evaluating the benchmark can be found in our [GitHub repository](https://github.com/nuprl/CanItEdit).