CELLO is a benchmark for evaluating theComplEx instruction understanding ability of Large Language MOdels systematically (AAAI 2024).
- We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios.
- We establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained.
- We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments.
conda create -n cello python=3.10.9
conda activate cello
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt
You can evaluate any desired model via the following scirpt eval.sh:
cd CELLO/
CUDA_VISIBLE_DEVICES=0 python code/eval.py --model_name chatglm --save_name chatglm
All the models are implemented in the folder code/evaluators. All the model results are in the folder results/.
The metrics for our designed four criteria can be calculated using the following script score.sh:
cd CELLO/
python code/score.py
All the scorers are implemented in the folder code/scorers. All the scoring results are in the folder scores/.
The collected data can be found in the data/. All samples have been anonymized.
@article{he2023cello,
title={Can Large Language Models Understand Real-World Complex Instructions?},
author={Qianyu He, Jie Zeng, Wenhao Huang, Lina Chen, Jin Xiao, Qianxi He, Xunzhe Zhou, Lida Chen, Xintao Wang, Yuncheng Huang, Haoning Ye, Zihan Li, Shisong Chen, Yikai Zhang, Zhouhong Gu, Jiaqing Liang, Yanghua Xiao},
journal={arXiv preprint arXiv:2309.09150},
year={2023},
url={https://arxiv.org/abs/2309.09150}
}