@misc{gao2023pal,
  title={PAL: Program-aided Language Models},
  author={Luyu Gao and Aman Madaan and Shuyan Zhou and Uri Alon and Pengfei Liu and Yiming Yang and Jamie Callan and Graham Neubig},
  year={2023},
  eprint={2211.10435},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

@misc{CoT,
  title={Chain-of-Thought Prompting Elicits Reasoning in Large Language Models},
  author={Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed Chi and Quoc Le and Denny Zhou},
  year={2023},
  eprint={2201.11903},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}


@misc{few-shot1,
  title={ReAct: Synergizing Reasoning and Acting in Language Models},
  author={Shunyu Yao and Jeffrey Zhao and Dian Yu and Nan Du and Izhak Shafran and Karthik Narasimhan and Yuan Cao},
  year={2023},
  eprint={2210.03629},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

@inproceedings{few-shot2,
 author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {1877--1901},
 publisher = {Curran Associates, Inc.},
 title = {Language Models are Few-Shot Learners},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},
 volume = {33},
 year = {2020}
}

@misc{ChatGPTexample,
    author = {ChatGPT},
    title = {{C}hat{G}{P}{T} {P}rogram-aided {L}angauge {M}odel {E}xample},
    howpublished = {\url{https://chat.openai.com/share/3a78d9db-9caa-4745-a417-0ef229bd7728}},
    year = {2023},
    note = {[Accessed 18-11-2023]},
}

@article{Demeter_Downey_2020,
    title={Just Add Functions: A Neural-Symbolic Language Model},
    volume={34},
    url={https://ojs.aaai.org/index.php/AAAI/article/view/6264},
    DOI={10.1609/aaai.v34i05.6264},
    abstractNote={<p>Neural network language models (NNLMs) have achieved ever-improving accuracy due to more sophisticated architectures and increasing amounts of training data. However, the inductive bias of these models (formed by the distributional hypothesis of language), while ideally suited to modeling most running text, results in key limitations for today’s models. In particular, the models often struggle to learn certain spatial, temporal, or quantitative relationships, which are commonplace in text and are second-nature for human readers. Yet, in many cases, these relationships can be encoded with simple mathematical or logical expressions. How can we augment today’s neural models with such encodings?</p><p>In this paper, we propose a general methodology to enhance the inductive bias of NNLMs by incorporating simple functions into a neural architecture to form a hierarchical neural-symbolic language model (NSLM). These functions explicitly encode symbolic deterministic relationships to form probability distributions over words. We explore the effectiveness of this approach on numbers and geographic locations, and show that NSLMs significantly reduce perplexity in small-corpus language modeling, and that the performance improvement persists for rare tokens even on much larger corpora. The approach is simple and general, and we discuss how it can be applied to other word classes beyond numbers and geography.</p>},
    number={05},
    journal={Proceedings of the AAAI Conference on Artificial Intelligence},
    author={Demeter, David and Downey, Doug},
    year={2020}, month={Apr.},
    pages={7634-7642}
}


@misc{pi2022reasoning,
    title={Reasoning Like Program Executors},
    author={Xinyu Pi and Qian Liu and Bei Chen and Morteza Ziyadi and Zeqi Lin and Qiang Fu and Yan Gao and Jian-Guang Lou and Weizhu Chen},
    year={2022},
    eprint={2201.11473},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{chen2023program,
    title={Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks},
    author={Wenhu Chen and Xueguang Ma and Xinyi Wang and William W. Cohen},
    year={2023},
    eprint={2211.12588},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}


@misc{Venturi, title={Pandasai}, url={https://docs.pandas-ai.com/},
    journal={PandasAI}, author={Venturi, Gabriele}}

@misc{zhao2023automatic,
    title={Automatic Model Selection with Large Language Models for Reasoning},
    author={James Xu Zhao and Yuxi Xie and Kenji Kawaguchi and Junxian He and Michael Qizhe Xie},
    year={2023},
    eprint={2305.14333},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@misc{kabra2023programaided,
    title={Program-Aided Reasoners (better) Know What They Know},
    author={Anubha Kabra and Sanketh Rangreji and Yash Mathur and Aman Madaan and Emmy Liu and Graham Neubig},
    year={2023},
    eprint={2311.09553},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}

@software{langchain,
    author = {Chase, Harrison},
    month = oct,
    title = {{LangChain}},
    url = {https://github.com/langchain-ai/langchain},
    year = {2022}
}

@misc{paranjape2023art,
    title={ART: Automatic multi-step reasoning and tool-use for large language models},
    author={Bhargavi Paranjape and Scott Lundberg and Sameer Singh and Hannaneh Hajishirzi and Luke Zettlemoyer and Marco Tulio Ribeiro},
    year={2023},
    eprint={2303.09014},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}


@misc{binder,
    title={Binding Language Models in Symbolic Languages},
    author={Zhoujun Cheng and Tianbao Xie and Peng Shi and Chengzu Li and Rahul Nadkarni and Yushi Hu and Caiming Xiong and Dragomir Radev and Mari Ostendorf and Luke Zettlemoyer and Noah A. Smith and Tao Yu},
    year={2023},
    eprint={2210.02875},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}