SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning
SciGLM is a suite of scientific language models able to conduct college-level scientific reasoning. Central to our approach is a novel self-reflective instruction annotation framework to address the data scarcity challenge in the science domain. This framework leverages existing LLMs to generate step-by-step reasoning for unlabelled scientific questions, followed by a process of self-reflective critic-and-revise. Applying this framework, we curated SciInstruct, a diverse and high-quality dataset encompassing physics, chemistry, math, and formal proofs.
SciInstruct
We construct the SciInstruct as follows:
| Subject | Math | Physics& Chemistry | Formal Proofs (Lean) | Total |
|---|---|---|---|---|
| # Number | 89,934 | 123,869 | 40,248 | 254,051 |
We release our data and model for public use. If you wish to use SciInstruct or SciGLM, you can download them from the following links.
Download data: [Google Drive] [Tsinghua Cloud]
Download model: [Hugging Face]
Training & Inference
Fine-tuning
You can use the SciGLM model through Huggingface's Transformers library.
git clone https://github.com/THUDM/SciGLM.git
cd SciGLM
pip install -r requirements.txt
To train the 6B model, run:
bash /path/training/finetune.sh
Inference
cd /path/to/inference
python cli_demo.py
Citation
If you find our work helpful, please kindly cite our paper:
@article{zhang2024sciglm,
title={Sciglm: Training scientific language models with self-reflective instruction annotation and tuning},
author={Zhang, Dan and Hu, Ziniu and Zhoubian, Sining and Du, Zhengxiao and Yang, Kaiyu and Wang, Zihan and Yue, Yisong and Dong, Yuxiao and Tang, Jie},
journal={arXiv preprint arXiv:2401.07950},
year={2024}
}