[40] W. Yidong, Y. Zhuohao, Z. Zhengran, Y. Linyi, H. Qiang, W. Cunxiang, C. Hao, J. Chaoya, X. Rui, W. Jindong, X. Xing, Y. Wei, Z. Shikun, and Z. Yue, "Pandalm: Reproducible and automated language model assessment." https://github.com/WeOpenML/PandaLM, 2023.
[41] M. Gao, J. Ruan, R. Sun, X. Yin, S. Yang, and X. Wan, “Human-like summarization evaluation with chatgpt,” arXiv preprint arXiv:2304.02554, 2023.
[42] A. R. Fabbri, W. Krysciński, B. McCann, C. Xiong, R. Socher, and D. Radev, "Summeval: Re-evaluating summarization evaluation," Transactions of the Association for Computational Linguistics, vol. 9, pp. 391-409, 2021.
[43] H. Chen, D. M. Vo, H. Takamura, Y. Miyao, and H. Nakayama, "Storyer: Automatic story evaluation via ranking, rating and reasoning," Journal of Natural Language Processing, vol. 30, no. 1, pp. 243-249, 2023.
[44] B. S. Bloom and D. R. Krathwohl, Taxonomy of educational objectives: The classification of educational goals. Book 1, Cognitive domain. longman, 2020.
[45] E. Choi, H. He, M. Iyyer, M. Yatskar, W.-t. Yih, Y. Choi, P. Liang, and L. Zettlemoyer, “Quac: Question answering in context,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2174–2184, 2018.
[46] S. Reddy, D. Chen, and C. D. Manning, "Coqa: A conversational question answering challenge," Transactions of the Association for Computational Linguistics, vol. 7, pp. 249-266, 2019.
[47] J. A. Campos, A. Otegi, A. Soroa, J. M. Deriu, M. Cieliebak, and E. Agirre, "Doqa-accessing domain-specific faqs via conversational qa," in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7302-7314, 2020.
[48] H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, E. Li, X. Wang, M. Dehghani, S. Brahma, et al., "Scaling instruction-finetuned language models," arXiv preprint arXiv:2210.11416, 2022.
[49] OpenAI, "Gpt-4 technical report," arXiv preprint arXiv:2303.08774, 2023.
[50] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al., "Sparks of artificial general intelligence: Early experiments with gpt-4," arXiv preprint arXiv:2303.12712, 2023.
[51] Anthropic, "Anthropic: Claude," 2023.
[52] Google, "Google: Bard," 2023.
[53] N. Muennighoff, T. Wang, L. Sutawika, A. Roberts, S. Biderman, T. L. Scao, M. S. Bari, S. Shen, Z.-X. Yong, H. Schoelkopf, et al., "Crosslingual generalization through multitask finetuning," arXiv preprint arXiv:2211.01786, 2022.
[54] H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, E. Li, X. Wang, M. Dehghani, S. Brahma, et al., "Scaling instruction-finetuned language models," arXiv preprint arXiv:2210.11416, 2022.
[55] A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia, W. L. Tam, Z. Ma, Y. Xue, J. Zhai, W. Chen, Z. Liu, P. Zhang, Y. Dong, and J. Tang, “GLM-130b: An open bilingual pre-trained model,” in The Eleventh International Conference on Learning Representations, 2023.
[56] J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, "Scaling laws for neural language models," arXiv preprint arXiv:2001.08361, 2020.
[57] LMSYS, "Lmsys.org: Chatbot arena," 2023.
[58] OpenAI, "Openai: New and improved embedding model," 2022.
[59] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al., "Language models are unsupervised multitask learners,"
[60] Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, "Self-instruct: Aligning language model with self generated instructions," arXiv preprint arXiv:2212.10560, 2022.