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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.

Table of Contents

• SciInstruct
• Training & Inference
• Leaderboard
• Citation

SciInstruct

We construct the SciInstruct as follows:

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

git clone https://github.com/THUDM/SciGLM.git
cd SciGLM
pip install -r requirements.txt

To train the 6B model, run:

cd /path/to/training
LR=3e-6
WR=0.0
LST=linear
epoch=2
MASTER_PORT=$(shuf -n 1 -i 10000-65535)

/path/to/deepspeed --include="localhost:0,1,2,3" --master_port $MASTER_PORT main.py \
    --deepspeed /path/to/deepspeed.json \
    --do_train \
    --train_file /path/to/SciInstruct.json \
    --eval_steps 200 \
    --prompt_column content \
    --response_column summary \
    --overwrite_cache \
    --model_name_or_path /path/to/ChatGLM3-6b-Base \
    --output_dir ./output/SciGLM-LR$LR-WR$WR-$LST-epoch$epoch \
    --overwrite_output_dir \
    --max_source_length 128 \
    --max_target_length 512 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 2 \
    --predict_with_generate \
    --num_train_epochs $epoch \
    --logging_steps 20 \
    --save_strategy epoch \
    --learning_rate $LR \
    --lr_scheduler_type $LST \
    --warmup_ratio $WR \
    --fp16

Inference

cd /path/to/inference
python cli_demo.py

Leaderboard

Results on Scientific Reasonings tasks

Results on Mathematical Reasoning

Citation

If you find our work helpful, please kindly cite our paper:

@misc{zhang2024sciglm,
      title={SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning}, 
      author={Dan Zhang and Ziniu Hu and Sining Zhoubian and Zhengxiao Du and Kaiyu Yang and Zihan Wang and Yisong Yue and Yuxiao Dong and Jie Tang},
      year={2024},
      eprint={2401.07950},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}