% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@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}
}

@article{DBLP:journals/corr/abs-2104-08696,
  author       = {Damai Dai and
                  Li Dong and
                  Yaru Hao and
                  Zhifang Sui and
                  Furu Wei},
  title        = {Knowledge Neurons in Pretrained Transformers},
  journal      = {CoRR},
  volume       = {abs/2104.08696},
  year         = {2021},
  url          = {https://arxiv.org/abs/2104.08696},
  eprinttype    = {arXiv},
  eprint       = {2104.08696},
  timestamp    = {Wed, 02 Jun 2021 10:17:40 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2104-08696.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{kaplan2020scaling,
  title={Scaling laws for neural language models},
  author={Kaplan, Jared and McCandlish, Sam and Henighan, Tom and Brown, Tom B and Chess, Benjamin and Child, Rewon and Gray, Scott and Radford, Alec and Wu, Jeffrey and Amodei, Dario},
  journal={arXiv preprint arXiv:2001.08361},
  year={2020}
}

@article{wei2022emergent,
  title={Emergent abilities of large language models},
  author={Wei, Jason and Tay, Yi and Bommasani, Rishi and Raffel, Colin and Zoph, Barret and Borgeaud, Sebastian and Yogatama, Dani and Bosma, Maarten and Zhou, Denny and Metzler, Donald and others},
  journal={arXiv preprint arXiv:2206.07682},
  year={2022}
}

@inproceedings{jawahar2019does,
  title={What does BERT learn about the structure of language?},
  author={Jawahar, Ganesh and Sagot, Beno{\^\i}t and Seddah, Djam{\'e}},
  booktitle={ACL 2019-57th Annual Meeting of the Association for Computational Linguistics},
  year={2019}
}

@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}
}

@article{carlini2022quantifying,
  title={Quantifying memorization across neural language models},
  author={Carlini, Nicholas and Ippolito, Daphne and Jagielski, Matthew and Lee, Katherine and Tramer, Florian and Zhang, Chiyuan},
  journal={arXiv preprint arXiv:2202.07646},
  year={2022}
}

@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."}

@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"}