[21] T. Nguyen, M. Rosenberg, X. Song, J. Gao, S. Tiwary, R. Majumder, and L. Deng, "Ms marco: A human generated machine reading comprehension dataset," choice, vol. 2640, p. 660, 2016.
[22] P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, "Squad: 100,000+ questions for machine comprehension of text," in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383-2392, 2016.
[23] P. Rajpurkar, R. Jia, and P. Liang, "Know what you don't know: Unanswerable questions for squad," in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 784-789, 2018.
[24] J. Berant, A. Chou, R. Frostig, and P. Liang, "Semantic parsing on freebase from question-answer pairs," in Proceedings of the 2013 conference on empirical methods in natural language processing, pp. 1533-1544, 2013.
[25] T. Mihaylov, P. Clark, T. Khot, and A. Sabharwal, "Can a suit of armor conduct electricity? a new dataset for open book question answering," in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2381-2391, 2018.
[26] A. Fan, Y. Jernite, E. Perez, D. Grangier, J. Weston, and M. Auli, “Eli5: Long form question answering,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3558–3567, 2019.
[27] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, "Bleu: a method for automatic evaluation of machine translation," in Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 311-318, 2002.
[28] S. Banerjee and A. Lavie, “Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pp. 65–72, 2005.
[29] A. Chen, G. Stanovsky, S. Singh, and M. Gardner, "Mocha: A dataset for training and evaluating generative reading comprehension metrics," in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6521-6532, 2020.
[30] C. Si, C. Zhao, and J. Boyd-Graber, “What's in a name? answer equivalence for open-domain question answering,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9623–9629, 2021.
[31] T. Sellam, D. Das, and A. Parikh, “Bleurt: Learning robust metrics for text generation,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7881–7892, 2020.
[32] R. Rei, C. Stewart, A. C. Farinha, and A. Lavie, “Comet: A neural framework for mt evaluation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2685–2702, 2020.
[33] W. Yuan, G. Neubig, and P. Liu, "Bartscore: Evaluating generated text as text generation," Advances in Neural Information Processing Systems, vol. 34, pp. 27263-27277, 2021.
[34] A. B. Sai, A. K. Mohankumar, and M. M. Khapra, "A survey of evaluation metrics used for nlg systems," ACM Computing Surveys (CSUR), vol. 55, no. 2, pp. 1-39, 2022.
[35] W. Zhao, M. Peyrard, F. Liu, Y. Gao, C. M. Meyer, and S. Eger, "Moverscore: Text generation evaluating with contextualized embeddings and earth mover distance," in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
[36] S. Gehrmann, E. Clark, and T. Sellam, “Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text,” arXiv preprint arXiv:2202.06935, 2022.
[37] M. Zhong, Y. Liu, D. Yin, Y. Mao, Y. Jiao, P. Liu, C. Zhu, H. Ji, and J. Han, "Towards a unified multi-dimensional evaluator for text generation," in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 2023-2038, 2022.
[38] W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, I. Stoica, and E. P. Xing, "Vicuna: An open-source chatbot impressing gpt-4 with  $90\%$  * chatgpt quality," March 2023.
[39] Y. Chen, R. Wang, H. Jiang, S. Shi, and R. Xu, “Exploring the use of large language models for reference-free text quality evaluation: A preliminary empirical study,” arXiv preprint arXiv:2304.00723, 2023.