[NeurIPS 2023]DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models This repository is the official implementation of DDCoT.
DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models
Ge Zheng∗ , Bin Yang∗, Jiajin Tang*, Hong-Yu Zhou, Sibei Yang†
*Equal contribution; †Corresponding Author
Install all required python dependencies:
pip install -r requirements.txt
Download the dataset from the following repository:
https://github.com/lupantech/ScienceQA/tree/main/data
Our trained model, extracted visual features and captions and generated rationales are available at Google Drive
To generate rationale for one sample，you can run the following code.
python rationale_generation.py
python main.py \
--model allenai/unifiedqa-t5-base \
--user_msg answer --img_type clip \
--bs 16 --eval_bs 4 --eval_acc 10 --output_len 64 --lr 1e-4 \
--prompt_format QCMG-A \
--output_dir model_path --use_generate --final_eval
python main.py \
--model allenai/unifiedqa-t5-base \
--user_msg answer --img_type clip \
--bs 8 --eval_bs 1 --eval_acc 10 --output_len 64 \
--final_eval --prompt_format QCMG-A \
--evaluate_dir model_path --use_generate
@article{zheng2023ddcot,
title={Ddcot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models},
author={Zheng, Ge and Yang, Bin and Tang, Jiajin and Zhou, Hong-Yu and Yang, Sibei},
journal={arXiv preprint arXiv:2310.16436},
year={2023}
}
This project is licensed under the Apache-2.0 License.
Part of our codes are adapted from ScienceQA.
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