|
--- |
|
annotations_creators: |
|
- expert-generated |
|
language_creators: |
|
- expert-generated |
|
language: |
|
- en |
|
license: |
|
- mit |
|
multilinguality: |
|
- monolingual |
|
size_categories: |
|
- n<1K |
|
source_datasets: |
|
- original |
|
task_categories: |
|
- text2text-generation |
|
task_ids: [] |
|
paperswithcode_id: canitedit |
|
pretty_name: CanItEdit |
|
tags: |
|
- code-generation |
|
- code |
|
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_lazy |
|
dtype: string |
|
- name: taxonomy |
|
struct: |
|
- name: change_kind |
|
dtype: string |
|
- name: libraries |
|
sequence: string |
|
- name: topic |
|
dtype: string |
|
splits: |
|
- name: test |
|
num_bytes: 564910 |
|
num_examples: 105 |
|
download_size: 250477 |
|
dataset_size: 564910 |
|
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 105 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: |
|
``` |
|
@inproceedings{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}, |
|
booktitle={The First International Workshop on Large Language Model for Code}, |
|
year={2024}, |
|
url={https://arxiv.org/abs/2312.12450} |
|
} |
|
``` |
|
|
|
## How To Evaluate |
|
All the code for evaluating the benchmark can be found in our [GitHub repository](https://github.com/nuprl/CanItEdit). |