DetermLR

This repository is the official implementation of our paper From indeterminacy to determinacy: augmenting logical reasoning capabilities with large language models.

Installation

pip install -r requirements.txt

Note: We recommend using guidance-0.0.64 for adopting GPT-3.5-turbo or GPT-4.

Setup OpenAI API

Please set your openai api key in os.environ["OPENAI_API_KEY"]="YOUR_API_KEY".

Make sure that your device is able to connect to OpenAI API.

Implementation

Quick start for implementing DetermLR to solve four logical reasoning tasks.

LogiQA

python logiqa-determlr.py

ProofWriter

python proofwriter-determlr.py

FOLIO

python folio-determlr.py

LogicalDeduction

python logicaldeduction-determlr.py

In addition to determlr, we also include baseline methods (cot, tot, cr) for four logical reasoning tasks. Directly run the script python {TASK}-{METHOD}.py to implement them.

Key Hyper-parameter settings:

For the implementation of our DetermLR, several important arguments are introduced as follows:

• propnum: number of generated determinate premises (choices={2,3,4,5})

• reasoningnum : final question number of vote choice {default=4,8,16}

• condition_divide: whether to divide premise into determinate and indeterminate {default=True}

• con_select : whether to include premise prioritization {default=True}

• memory: whether to infer premise generating history or not {default=True}

• useful_judgement: whether to include useful_judgement validation {default=True}

• global_validation: whether to include global_validation, not required on ProofWriter.

Acknowledgement

Partial credit to previous reprostories: Guidance and Cumulative Reasoning.

Citations

Please cite the paper and star this repo if you use DetermLR and find it interesting/useful, thanks!

Feel free to contact xuwk266@gmail.com for any questions.

@article{sun2023indeterminacy,
      title={From Indeterminacy to Determinacy: Augmenting Logical Reasoning Capabilities with Large Language Models}, 
      author={Sun, Hongda and Xu, Weikai and Liu, Wei and Luan, Jian and Wang, Bin and Shang, Shuo and Wen, Ji-Rong and Yan, Rui},
      journal={arXiv preprint arXiv:2310.18659},
      year={2023}
}