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.

@inproceedings{he2024can,
  title={Can Large Language Models Understand Real-World Complex Instructions?},
  author={He, Qianyu and Zeng, Jie and Huang, Wenhao and Chen, Lina and Xiao, Jin and He, Qianxi and Zhou, Xunzhe and Liang, Jiaqing and Xiao, Yanghua},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={16},
  pages={18188--18196},
  year={2024}
}