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.

See our paper for more.

This repository provides code for evaluating models on the benchmark, and the code to reproduce EditPackFT and EditCoder, a dataset and a LLM built for instructional code editing.

The CanItEdit benchmark dataset, EditCoder model, and EditPackFT dataset can be found on HuggingFace:

• CanItEdit: https://huggingface.co/datasets/nuprl/CanItEdit
• EditCoder: https://huggingface.co/nuprl/EditCoder-6.7b-v1
• EditPackFT: https://huggingface.co/datasets/nuprl/EditPackFT

Cloning the repository

It is very important to clone this repository and initialize all submodule recursively. This can be done with the following command:

git clone --recurse-submodules https://github.com/nuprl/CanItEdit

Structure

• ./benchmark contains the CanItEdit benchmark dataset and code for generating and evaluating completions
• ./editcoder contains code to train an EditCoder model
• ./editpackft contains code to reproduce the EditPackFT dataset
• ./requirements.txt contains the requirements for running the code in this repository

Citation

If you use this code or the CanItEdit benchmark, please cite our paper:

@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}
}