Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning

This repository provides prompts, LLM results and code implementation for our paper Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning, which is accepted by ICLR'24!

Please cite our paper if you find our work/code helpful!

@article{Yue2023LargeLM,
  title={Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning},
  author={Murong Yue and Jie Zhao and Min Zhang and Liang Du and Ziyu Yao},
  journal={ArXiv},
  year={2023},
  volume={abs/2310.03094},
  url={https://api.semanticscholar.org/CorpusID:263671564}
}

1. Setup

pip install -r requirements.txt

2. Results and Prompts

All results with different temperatures are listed in the folder. All prompts are in Evaluation/prompting.

3. Evaluation Result

To evaluate the result of any setting, run the code

python Evaluation/${FILE}