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Official implementation for "GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Models" (stay tuned & more will be updated)

Python 100.00%

glape's Introduction

GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Models

Supercharge your prompt optimization without the hassle of elusive gold labels!

Introducing GLaPE (Gold Label-agnostic Prompt Evaluation) – a groundbreaking methodology leveraging self-consistency and mutual-consistency refinement.

Our GLaPE-based prompt optimization yields prompts comparable to accuracy-based ones on six popular datasets.

Check our paper for more information.

Requirements

Make sure you have Python>=3.8 installed on your machine.

pip install torch==1.8.2+cu111 torchtext==0.9.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
pip install -r requirements.txt

Quick Start

Set your OpenAI API key first

GLaPE-based prompt optimization (Ours):

python main.py --dataset=gsm8k \
--test_dataset_size=50

Accuracy-based prompt optimization (OPRO):

python main.py --dataset=gsm8k \
--test_dataset_size=50 \
--evaluation_metric=accuracy

Key arguments

--eval_dataset_size # The size of dataset to evaluate the prompt. To save budget, set it smaller.
--test_dataset_size # The size of dataset to test the optimal prompt. Default 0, which means use the whole dataset.
--cot_generate_times * --cot_generate_num # The total number of new prompts generated in the optimization trajectory.

Citation

@misc{zhang2024glape,
      title={GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Model}, 
      author={Xuanchang Zhang and Zhuosheng Zhang and Hai Zhao},
      year={2024},
      eprint={2402.02408},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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