Awesome

CLMEEval

This repository contains the code for our paper, "Model Editing for LLMs4Code: How Far Are We?", accepted at ICSE 2025.

Requirements

Pip Installation

Note: Please use Python 3.9+ for CLMEEval. To get started, simply install conda and run:

conda create -n clme python=3.9.7
...
pip install -r requirements.txt

Data Download

The CNLE and CSNE datasets are available on Zenodo.

• Note: If you are using editing techniques that require training (e.g., MALMEN and A-GRACE), please divide the dataset into training and testing sets first.

Run Experiments

Editing CodeLlama on the CNLE dataset using A-GRACE

First, use train_agrace_encoder.sh to train the encoder for A-GRACE. Then, use the following script to edit CodeLlama on the CNLE dataset using A-GRACE.

python edit_main.py \
    --editing_method=AGRACE \
    --data_dir=./[your splitted data dir] \
    --data_set=EditConala \
    --hparams_dir=./hparams/AGRACE/codellama-7b.yaml

Run other experiments

Use the following script template to run experiments:

python edit_main.py \
--editing_method=[Editing Approach] \
--hparams_dir=[Hparams Path]

All run scripts are available in run_scripts.

Note: Ensure that the paths are set appropriately on your device.

Acknowledgement

This project is derived from EasyEdit