TianduoWang/MsAT

MsAT: Learning Multi-step Reasoning by Solving Arithmetic Tasks

Overview

Motivated by large language models' impressive reasoning abilities elicited by the chain-of-thought prompting (Wei et al., 2022), we propose to inject multi-step reasoning ability into relatively small LMs (e.g., RoBERTa) by pre-training them on a synthetic dataset MsAT.

Our experiments are conducted with two backbone models: a Seq2Seq model RoBERTaGen which augments RoBERTa with a transformer decoder, and a Seq2DAG model DeductReasoner that combines RoBERTa with a directed-acyclic-graph decoder.

Install dependencies

All our experiments are conducted with Python 3.8 and PyTorch v1.12.0. Please first install PyTorch v1.12.0 from the official link. To install other dependencies, please run the following script:

pip install -r requirements.txt

Pre-training on MsAT

The core idea of our method is to inject multi-step reasoning skills into models by pre-training them on the proposed synthetic dataset MsAT.

We have released the weights of pre-trained models on Huggingface Model Hub.

Models	MsAT Acc.
Tianduo/MsAT-DeductReasoner	99.0
Tianduo/MsAT-RoBERTaGen	96.1

You may simply load the pre-trained weights using the following code before fine-tuning on downstream MWP tasks (take RoBERTaGen as an example).

from robertagen.model import RoBERTaGen, HFRoBERTaGen, RoBERTaGenConfig

hf_config = RoBERTaGenConfig.from_pretrained('Tianduo/MsAT-RoBERTaGen')
model_args = RoBERTaGen.parse_model_args(hf_config.to_diff_dict())
model = HFRoBERTaGen.from_pretrained(
   'Tianduo/MsAT-RoBERTaGen', 
   config=hf_config, 
   pytorch_model=RoBERTaGen(model_args))

Or you can train your own MsAT-RoBERTaGen using the following code:

python robertagen/train.py -c robertagen/config/msat.yaml

All the necessary training hyperparameters are organized in YAML files.

MsAT construction (optional)

One of the advantages of using synthetic data for pre-training is the customizability. Run the following code to create a new MsAT dataset:

python data/make_msat_data.py --total_num 85000 --train_num 80000 --difficulty 2.4

Arguments are explained here:

--total_num number of training data + number of test data
--train_num number of training data
--difficulty the difficulty level of MsAT

Fine-tuning on Math Word Problem datasets

For example, to fine-tune MsAT-RoBERTaGen on SVAMP, we can run the following command:

python robertagen/train.py -c robertagen/config/svamp.yaml

Evaluation

Our fine-tuned model checkpoints (on SVAMP) are released on Huggingface Model Hub.

Models	SVAMP Acc.
Tianduo/MsAT-DeductReasoner-SVAMP	48.8
Tianduo/MsAT-RoBERTaGen-SVAMP	39.9

Run the following code to evaluate the models:

python deductreasoner/evaluate.py -m Tianduo/MsAT-DeductReasoner-SVAMP -d svamp

python robertagen/evaluate.py -m Tianduo/MsAT-RoBERTaGen-SVAMP -d svamp

Arguments for the evaluation scripts are explained here:

-m name or address of the model checkpoint
-d evaluation dataset

Citation

@inproceedings{wang2023msat,
   title={Learning Multi-step Reasoning by Solving Arithmetic Tasks},
   author={Wang, Tianduo and Lu, Wei},
   booktitle={Proceedings of ACL},
   year={2023}
}

TianduoWang / MsAT