Deductive Beam Search
Decoding Deducible Rationale for Chain-of-Thought Reasoning

Source Code of paper Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning.

Quick Start

Clone repo:

git clone https://github.com/OSU-NLP-Group/Deductive-Beam-Search.git
cd Deductive-Beam-Search
mkdir outputs

Install requirements:

conda create --name dbs python=3.10
conda activate dbs
pip install -r requirements.txt

Training:

Stage 1:

python src/train.py \
    --train_datapath your_data_path_stage1 \
    --experiment_name your_experiment_name_stage1 \
    --batch_size 8 --gradient_accumulation_steps 16 \
    --learning_rate 1e-5

Stage 2:

python src/train.py \
    --train_datapath your_data_path_stage2 \
    --experiment_name your_experiment_name_stage2 \
    --batch_size 8 --gradient_accumulation_steps 16 \
    --learning_rate 1e-7

Inference:

DECODER_PATH=your_model_path
DATASETS=("gsm8k" "svamp") # add datasets you want to test

for TEST_DATASET in ${DATASETS[@]};
do
    python src/test.py \
        --test_dataset $TEST_DATASET \
        --output_dir outputs/$TEST_DATASET \
        --decoder_path $DECODER_PATH \
        --decode_strategy beam_search \
        --num_beams 5 \
        --num_sampling 10 \
        --num_gpus_decode 4 # how many gpus for inference
done

Data

Training Data

All training data synthesized is in data/ folder. The training file data/stage1/train.json is used for training a general deductive verifier. For stage 2, the arithmetic and symbolic verifier is trained on data/stage2/arithmetic/train.split.*.json, and the commonsense verifier is trained on data/stage2/commonsense/train.json.

Checkpoints

We provide the checkpoint of a general deductive verifier, please download from this link. You can use it to continue-train on our data or train on your own data.

Data Generation

The complete process of data construction is in src/generate_data.py. If you want to generate data on your own, please modify the data loading part. After modifying, you can run python src/generate_data.py to generate data for your own domains.

[TODO] We will improve the code for easier modification and usage.

Contact

If you have any problems, please contact Tinghui Zhu and Kai Zhang.

Citation Information

If you find our codes and data useful, please consider citing our paper:

@article{zhu2024deductive,
  title={Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning},
  author={Zhu, Tinghui and Zhang, Kai and Xie, Jian and Su, Yu},
  journal={arXiv preprint arXiv:2401.17686},
  year={2024}
}

OSU-NLP-Group/Deductive-Beam-Search

Deductive Beam SearchDecoding Deducible Rationale for Chain-of-Thought Reasoning