[33] Q. Jin, Z. Yuan, G. Xiong, Q. Yu, H. Ying, C. Tan, M. Chen, S. Huang, X. Liu, and S. Yu. Biomedical question answering: A survey of approaches and challenges. ACM Comput. Surv., 55(2), jan 2022. ISSN 0360-0300. doi: 10.1145/3490238. URL https://doi.org/10.1145/3490238
[34] V. Karpukhin, B. Oguz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih. Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769-6781, 2020.
[35] O. Khattab and M. Zaharia. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pages 39-48, 2020.
[36] T. Khot, D. Khashabi, K. Richardson, P. Clark, and A. Sabharwal. Text modular networks: Learning to decompose tasks in the language of existing models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1264–1279, 2021.
[37] R. M. Kinney, C. Anastasiades, R. Authur, I. Beltagy, J. Bragg, A. Buraczynski, I. Cachola, S. Candra, Y. Chandrasekhar, A. Cohan, M. Crawford, D. Downey, J. Dunkelberger, O. Etzioni, R. Evans, S. Feldman, J. Gorney, D. W. Graham, F. Hu, R. Huff, D. King, S. Kohlmeier, B. Kuehl, M. Langan, D. Lin, H. Liu, K. Lo, J. Lochner, K. MacMillan, T. Murray, C. Newell, S. Rao, S. Rohatgi, P. L. Sayre, Z. Shen, A. Singh, L. Soldaini, S. Subramanian, A. Tanaka, A. D. Wade, L. M. Wagner, L. L. Wang, C. Wilhelm, C. Wu, J. Yang, A. Zamarron, M. van Zuylen, and D. S. Weld. The semantic scholar open data platform. ArXiv, abs/2301.10140, 2023.
[38] T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa. Large language models are zero-shot reasoners. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 22199-22213. Curran Associates, Inc., 2022. URL https://proceedings.neurips.cc/paper_files/paper/2022/file/8bb0d291acd4acf06ef112099c16f326-Paper-Conference.pdf
[39] L. Kuhn, Y. Gal, and S. Farquhar. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. arXiv preprint arXiv:2302.09664, 2023.
[40] T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. Parikh, C. Alberti, D. Epstein, I. Polosukhin, M. Kelcey, J. Devlin, K. Lee, K. N. Toutanova, L. Jones, M.-W. Chang, A. Dai, J. Uszkoreit, Q. Le, and S. Petrov. Natural questions: a benchmark for question answering research. Transactions of the Association of Computational Linguistics, 2019.
[41] J. Lee, K. Lee, and J. G. Kim. Personalized academic research paper recommendation system. arXiv preprint arXiv:1304.5457, 2013.
[42] Z. Li, J. Cai, S. He, and H. Zhao. Seq2seq dependency parsing. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3203-3214, 2018.
[43] B. Lin, Z. Yao, J. Shi, S. Cao, B. Tang, S. Li, Y. Luo, J. Li, and L. Hou. Dependency parsing via sequence generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7339-7353, 2022.
[44] M. Lissandrini, D. Mottin, T. Palpanas, and Y. Velegrakis. Example-based search: A new frontier for exploratory search. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'19, page 1411-1412, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450361729. doi: 10.1145/3331184.3331387. URL https://doi.org/10.1145/3331184.3331387
[45] P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1-35, 2023.
[46] T. Liu, Y. Zhang, C. Brockett, Y. Mao, Z. Sui, W. Chen, and B. Dolan. A token-level reference-free hallucination detection benchmark for free-form text generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6723–6737, Dublin, Ireland, May 2022. Association for Computational Linguistics. doi: 10.18653/v1/2022.acl-long.464. URL https://aclanthology.org/2022.acl-long.464