@article{waks1997lateral,
  title={Lateral thinking and technology education},
  author={Waks, Shlomo},
  journal={Journal of Science Education and Technology},
  volume={6},
  pages={245--255},
  year={1997},
  publisher={Springer}
}

@misc{jiang2023brainteaser,
      title={BRAINTEASER: Lateral Thinking Puzzles for Large Language Models}, 
      author={Yifan Jiang and Filip Ilievski and Kaixin Ma and Zhivar Sourati},
      year={2023},
      eprint={2310.05057},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{jiang-semeval-2024-brainteaser, title = "SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense", author = "Jiang, Yifan and Ilievski, Filip and Ma, Kaixin", booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation", year = "2024", publisher = "Association for Computational Linguistics"}

@InProceedings{jiang-ilievski-ma:2024:SemEval2024,
  author    = {Jiang, Yifan  and  Ilievski, Filip  and  Ma, Kaixin},
  title     = {SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense},
  booktitle      = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},
  month          = {June},
  year           = {2024},
  address        = {Mexico City, Mexico},
  publisher      = {Association for Computational Linguistics},
  pages     = {1996--2010},
  abstract  = {While vertical thinking relies on logical and commonsense reasoning, lateral thinking requires systems to defy commonsense associations and overwrite them through unconventional thinking. Lateral thinking has been shown to be challenging for current models but has received little attention. A recent benchmark, BRAINTEASER, aims to evaluate current models' lateral thinking ability in a zero-shot setting. In this paper, we split the original benchmark to also support fine-tuning setting and present SemEval Task 9, BRAINTEASER(S), the first task at this competition designed to test the system's reasoning and lateral thinking ability. As a popular task, BRAINTEASER(S)'s two subtasks receive 483 team submissions from 182 participants during the competition. This paper provides a fine-grained system analysis of the competition results, together with a reflection on what this means for the ability of the systems to reason laterally. We hope that the BRAINTEASER(S) subtasks and findings in this paper can stimulate future work on lateral thinking and robust reasoning by computational models},
  url       = {https://aclanthology.org/2024.semeval2024-1.271}
}

@inproceedings{jiang-etal-2023-brainteaser, title = "{BRAINTEASER}: Lateral Thinking Puzzles for Large Language Models", author = "Jiang, Yifan and Ilievski, Filip and Ma, Kaixin and Sourati, Zhivar", editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.emnlp-main.885", doi = "10.18653/v1/2023.emnlp-main.885", pages = "14317--14332", abstract = "The success of language models has inspired the NLP community to attend to tasks that require implicit and complex reasoning, relying on human-like commonsense mechanisms. While such vertical thinking tasks have been relatively popular, lateral thinking puzzles have received little attention. To bridge this gap, we devise BrainTeaser: a multiple-choice Question Answering task designed to test the model{'}s ability to exhibit lateral thinking and defy default commonsense associations. We design a three-step procedure for creating the first lateral thinking benchmark, consisting of data collection, distractor generation, and generation of adversarial examples, leading to 1,100 puzzles with high-quality annotations. To assess the consistency of lateral reasoning by models, we enrich BrainTeaser based on a semantic and contextual reconstruction of its questions. Our experiments with state-of-the-art instruction- and commonsense language models reveal a significant gap between human and model performance, which is further widened when consistency across adversarial formats is considered. We make all of our code and data available to stimulate work on developing and evaluating lateral thinking models."}

@article{team2023gemini,
  title={Gemini: a family of highly capable multimodal models},
  author={Team, Gemini and Anil, Rohan and Borgeaud, Sebastian and Wu, Yonghui and Alayrac, Jean-Baptiste and Yu, Jiahui and Soricut, Radu and Schalkwyk, Johan and Dai, Andrew M and Hauth, Anja and others},
  journal={arXiv preprint arXiv:2312.11805},
  year={2023}
}

@inproceedings{ma2021knowledge,
  title={Knowledge-driven data construction for zero-shot evaluation in commonsense question answering},
  author={Ma, Kaixin and Ilievski, Filip and Francis, Jonathan and Bisk, Yonatan and Nyberg, Eric and Oltramari, Alessandro},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={15},
  pages={13507--13515},
  year={2021}
}

@misc{xie2023olagpt,
      title={OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities}, 
      author={Yuanzhen Xie and Tao Xie and Mingxiong Lin and WenTao Wei and Chenglin Li and Beibei Kong and Lei Chen and Chengxiang Zhuo and Bo Hu and Zang Li},
      year={2023},
      eprint={2305.16334},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{huang2023lateval,
      title={LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles}, 
      author={Shulin Huang and Shirong Ma and Yinghui Li and Mengzuo Huang and Wuhe Zou and Weidong Zhang and Hai-Tao Zheng},
      year={2023},
      eprint={2308.10855},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@incollection{dingli2008thinking,
  title={Thinking outside the box: Edward de Bono's lateral thinking},
  author={Dingli, Sandra},
  booktitle={The Routledge companion to creativity},
  pages={338--350},
  year={2008},
  publisher={Routledge}
}

@article{brown2020language,
  title={Language models are few-shot learners},
  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 others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={1877--1901},
  year={2020}
}

@misc{nori2023generalist,
      title={Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine}, 
      author={Harsha Nori and Yin Tat Lee and Sheng Zhang and Dean Carignan and Richard Edgar and Nicolo Fusi and Nicholas King and Jonathan Larson and Yuanzhi Li and Weishung Liu and Renqian Luo and Scott Mayer McKinney and Robert Osazuwa Ness and Hoifung Poon and Tao Qin and Naoto Usuyama and Chris White and Eric Horvitz},
      year={2023},
      eprint={2311.16452},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{devlin2019bert,
      title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}, 
      author={Jacob Devlin and Ming-Wei Chang and Kenton Lee and Kristina Toutanova},
      year={2019},
      eprint={1810.04805},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{meng2024divide,
      title={Divide and Conquer for Large Language Models Reasoning}, 
      author={Zijie Meng and Yan Zhang and Zhaopeng Feng and Yang Feng and Gaoang Wang and Joey Tianyi Zhou and Jian Wu and Zuozhu Liu},
      year={2024},
      eprint={2401.05190},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{achiam2023gpt,
  title={Gpt-4 technical report},
  author={Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and Anadkat, Shyamal and others},
  journal={arXiv preprint arXiv:2303.08774},
  year={2023}
}

@misc{singhal2023expertlevel,
      title={Towards Expert-Level Medical Question Answering with Large Language Models}, 
      author={Karan Singhal and Tao Tu and Juraj Gottweis and Rory Sayres and Ellery Wulczyn and Le Hou and Kevin Clark and Stephen Pfohl and Heather Cole-Lewis and Darlene Neal and Mike Schaekermann and Amy Wang and Mohamed Amin and Sami Lachgar and Philip Mansfield and Sushant Prakash and Bradley Green and Ewa Dominowska and Blaise Aguera y Arcas and Nenad Tomasev and Yun Liu and Renee Wong and Christopher Semturs and S. Sara Mahdavi and Joelle Barral and Dale Webster and Greg S. Corrado and Yossi Matias and Shekoofeh Azizi and Alan Karthikesalingam and Vivek Natarajan},
      year={2023},
      eprint={2305.09617},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{hendrycks2021measuring,
      title={Measuring Massive Multitask Language Understanding}, 
      author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
      year={2021},
      eprint={2009.03300},
      archivePrefix={arXiv},
      primaryClass={cs.CY}
}

@inbook{inbook,
author = {Martinez-Gil, Jorge and Freudenthaler, Bernhard and Tjoa, A Min},
year = {2019},
month = {08},
pages = {138-148},
title = {Multiple Choice Question Answering in the Legal Domain Using Reinforced Co-occurrence},
isbn = {978-3-030-27614-0},
doi = {10.1007/978-3-030-27615-7_10}
}