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<h1 align="center"> Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up </h1>RoT improves reasoning accuracy and efficiency while minimizing computational costs, leveraging Preference-Guided Reverse Reasoning and a Cognitive Preference Manager to optimally explore LLM reasoning with cognitive preferences.
Model Architecture
<img src="./asset/Reversal_Of_Thought.png">Demo for Preference-Guided Reverse Reasoning
🎉🎉🎉 reversal_demo.py
from utils.llm_utils import *
from utils.prompt import *
pipeline=Pipeline(model_id=model_id, base_url=base_url, api_key=api_key, prob=True)
demos = "Input:... Output:..." #Suggest 2-shot Demos
llm_taste=rot_pipeline( pipeline, reversal_of_thought, demos=demos, warmup=5)
What might reversal_demo.py be used for?
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Enhance LLM-Preferred Prompts for Task Solutions
Refines prompts to align with LLM-preferred strategies, optimizing task-solving efficiency. -
Potential for Creating Diverse QA Datasets
Generates varied question-answer pairs to improve dataset diversity.
Citation
If you find our work useful for your research, please kindly cite our paper as follows:
@article{yuan2024reversal,
title={Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up},
author={Yuan, Jiahao and Du, Dehui and Zhang, Hao and Di, Zixiang and Naseem, Usman},
journal={arXiv preprint arXiv:2410.12323},
year={2024}
}