[30] A. Jacovi, A. Caciularu, O. Goldman, and Y. Goldberg. Stop uploading test data in plain text: Practical strategies for mitigating data contamination by evaluation benchmarks. ArXiv preprint, abs/2305.10160, 2023.
[31] M. I. Jordan. Artificial intelligence—the revolution hasn't happened yet. Harvard Data Science Review, 1(1):1-9, 2019.
[32] J. Kaddour, J. Harris, M. Mozes, H. Bradley, R. Raileanu, and R. McHardy. Challenges and applications of large language models. *ArXiv preprint*, abs/2307.10169, 2023.
[33] G. Ke, D. He, and T. Liu. Rethinking positional encoding in language pre-training. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021.
[34] N. Kohn and S. M. Smith. Partly versus completely out of your mind: Effects of incubation and distraction on resolving fixation. The Journal of Creative Behavior, 43(2):102-118, 2009.
[35] H. Levesque, E. Davis, and L. Morgenstern. The winograd schema challenge. In Thirteenth international conference on the principles of knowledge representation and reasoning, 2012.
[36] M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871-7880, Online, 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.acl-main.703.
[37] P. S. H. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Roektäschel, S. Riedel, and D. Kiela. Retrieval-augmented generation for knowledge-intensive NLP tasks. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
[38] P. Liang, R. Bommasani, T. Lee, D. Tsipras, D. Soylu, M. Yasunaga, Y. Zhang, D. Narayanan, Y. Wu, A. Kumar, et al. Holistic evaluation of language models. ArXiv preprint, abs/2211.09110, 2022.
[39] C.-Y. Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74-81, Barcelona, Spain, 2004. Association for Computational Linguistics.
[40] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. Roberta: A robustly optimized bert pretraining approach. ArXiv preprint, abs/1907.11692, 2019.
[41] N. Lu, S. Liu, R. He, and K. Tang. Large language models can be guided to evade AI-generated text detection. ArXiv preprint, abs/2305.10847, 2023.
[42] A. S. Luchins and E. H. Luchins. Rigidity of behavior: A variational approach to the effect of Einstellung. 1959.
[43] M. Marko, D. Michalko, and I. Riečansky. Remote associates test: An empirical proof of concept. Behavior research methods, 51:2700-2711, 2019.
[44] S. Mednick. The associative basis of the creative process. Psychological review, 69(3):220, 1962.
[45] S. A. Mednick. The remote associates test. The Journal of Creative Behavior, 1968.
[46] G. A. Miller. WordNet: A lexical database for English. In Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992, 1992.
[47] R. Navigli. Word sense disambiguation: A survey. ACM computing surveys (CSUR), 41(2): 1-69, 2009.
[48] OpenAI. GPT-4 technical report. ArXiv preprint, abs/2303.08774, 2023.