This is the official implementation of the IEEE ICHI 2024 paper:
Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts
git clone git@github.com:trust-nlp/CoI-Psychotherapy.git
conda create -n [YOUR_ENV] python=3.10
conda activate [YOUR_ENV]
cd CoI-Psychotherapy
pip install -r requirements.txt
Please refer to data/README.md for building your own dataset.
And modify your dataset path in src/llmtuner/eval/MMCBevaluator.py.
CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
--model_name_or_path path_to_LLM \
--template vanilla \
--task mmlu \
--split test \
--lang [Prompting method] \
--n_shot 0 \
--batch_size 4
If this work is helpful, please kindly cite as:
@article{han2024chainofinteraction,
title={Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts},
author={Guangzeng Han and Weisi Liu and Xiaolei Huang and Brian Borsari},
journal={arXiv preprint arXiv:2403.13786},
year={2024},
eprint={2403.13786},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2403.13786}
}
This project is a fork of hiyouga/LLaMA-Factory. We are grateful for their work and contributions to the LLM community. This fork aims to evaluate the performance of multiple prompting methods for automated coding of motivational interviews. Please visit the original repository to learn more about the project and support the original creators.