Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ryanlingo/REAP-LLM-Problem-Solving
Enhance LLM problem-solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting. This repo includes the REAP prompt framework, code for reproducing experiments, datasets, and supplementary materials, supporting structured reasoning and dynamic context generation in LLMs.
https://github.com/ryanlingo/REAP-LLM-Problem-Solving
Last synced: 3 days ago
JSON representation
Enhance LLM problem-solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting. This repo includes the REAP prompt framework, code for reproducing experiments, datasets, and supplementary materials, supporting structured reasoning and dynamic context generation in LLMs.
- Host: GitHub
- URL: https://github.com/ryanlingo/REAP-LLM-Problem-Solving
- Owner: ryanlingo
- License: cc0-1.0
- Created: 2024-11-03T13:10:53.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-03T13:29:34.000Z (2 months ago)
- Last Synced: 2024-11-03T14:22:49.770Z (2 months ago)
- Size: 317 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome_ai_agents - Reap-Llm-Problem-Solving - Enhance LLM problem-solving with REAP - Reflection, Explicit Problem Deconstruction, and Advanced Prompting. This repo includes the REAP p… (Building / Datasets)
README
# REAP-LLM-Problem-Solving
## Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting
This repository contains resources and materials for the research paper: **"[Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting](https://arxiv.org/abs/2409.09415)"**. The REAP framework improves problem-solving capabilities in large language models (LLMs) by guiding them through structured, context-aware reasoning processes, enhancing logical consistency and clarity.
## Repository Contents
- **README.md**: Overview of the repository and instructions for usage.
- **REAP_Prompt.md**: The detailed REAP prompt framework for structured problem-solving.
- **data/**: Contains the questions from the linguistic benchmark dataset used in the study. This dataset originates from [Easy Problems That LLMs Get Wrong](https://arxiv.org/abs/2405.19616). Please cite this paper if you use these questions.
## Usage
To use the REAP prompt:
1. Insert your problem into the REAP prompt at the end in the section labeled ``.
2. Run the REAP prompt on any of your available large language models (LLMs) to guide the model through structured problem-solving steps.
If you would like to try REAP on the problems we used in our benchmark, the questions are available in the `data/` folder. These questions are sourced from [Easy Problems That LLMs Get Wrong](https://arxiv.org/abs/2405.19616). Please make sure to cite this paper if you use this dataset.
## Citing This Work
If you use this REAP framework in your research, please cite our paper:
```bibtex
@article{lingo2024REAP,
title={Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting},
author={Lingo, Ryan and Arroyo, Martin and Chhajer, Rajeev},
journal={arXiv preprint arXiv:2409.09415},
year={2024},
url={https://arxiv.org/abs/2409.09415}
}
```
For any questions used from the `data/` folder, please also cite:
```bibtex
@article{williams2024EasyProblems,
title={Easy Problems That LLMs Get Wrong},
author={Williams, S and Huckle, J},
journal={arXiv preprint arXiv:2405.19616},
year={2024},
url={https://arxiv.org/abs/2405.19616}
}
```
## License
This repository is licensed under the **Creative Commons Zero v1.0 Universal (CC0)** license, dedicating the work to the public domain. See the LICENSE file for details.