Distilling ChatGPT for Explainable Automated Student Answer Assessment

Jiazheng Li, Lin Gui, Yuxiang Zhou, David West, Cesare Aloisi and Yulan He

Abstract

Providing explainable and faithful feedback is crucial for automated student answer assessment. In this paper, we introduce a novel framework that explores using ChatGPT, a cutting-edge large language model, for the concurrent tasks of student answer scoring and rationale generation. We identify the appropriate instructions by prompting ChatGPT with different templates to collect the rationales, where inconsistent rationales are refined to align with marking standards. The refined ChatGPT outputs enable us to fine-tune a smaller language model that simultaneously assesses student answers and provides rationales. Extensive experiments on the benchmark dataset show that the proposed method improves the overall QWK score by 11% compared to ChatGPT. Furthermore, our thorough analysis and human evaluation demonstrate that the rationales generated by our proposed method are comparable to those of ChatGPT. Our approach provides a viable solution to achieve explainable automated assessment in education.

Getting Started

Project structure:

CUE
├── README.md
├── environment.yml     # Conda environment     
├── train.py            # Train rationale generation model
├── train_cls.py        # Train classification baseline
├── dataset
│   ├── ...
│   └── README.md       # Pls read this for dataset detail
└── aera
    └── ...

Creating an environment from an environment.yml file

conda env create -f environment.yml

Define the path you save models

In the constants.py, you will find two constant paths defined at the top:

DATAFOLDER = "/path/to/the/folder/"
CACHEFOLDER = "/path/to/the/folder/transfomers_cache/"

The DATAFOLDER is the path to save all the trianed models and the CACHEFOLDER is the path used to store transformers package's cached models.

An example to use AERA

Finetune classification baseline

python train_cls.py -d $dataset_name -b $batch_size -e $num_of_epochs -m $base_model -p $output_path -r $rounds_to_train

Example:

python train_cls.py -d asap-1 -b 16 -e 30 -m bert-base-uncased -p bert-base-uncased-asap-1 -r 5

Finetune rationale generation model

python train.py -d $dataset_name -b $batch_size -e $num_of_epochs -p $output_path -r $rounds_to_train

Example:

python train_cls.py -d asap-1 -b 8 -e 30 -p longt5_large-asap-1 -r 5

Cite our work

@misc{li2023distilling,
      title={Distilling ChatGPT for Explainable Automated Student Answer Assessment}, 
      author={Jiazheng Li and Lin Gui and Yuxiang Zhou and David West and Cesare Aloisi and Yulan He},
      year={2023},
      eprint={2305.12962},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}