[47] Y. Lu, Y. Liu, J. Liu, Y. Shi, Z. Huang, S. F. Y. Sun, H. Tian, H. Wu, S. Wang, D. Yin, and H. Wang. Ernie-search: Bridging cross-encoder with dual-encoder via self on-the-fly distillation for dense passage retrieval, 2022.
[48] S. MacAvaney and L. Soldaini. One-shot labeling for automatic relevance estimation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '23, page 2230-2235, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9781450394086. doi: 10.1145/3539618.3592032. URL https://doi.org/10.1145/3539618.3592032.
[49] S. MacAvaney, S. Feldman, N. Goharian, D. Downey, and A. Cohan. ABNIRML: Analyzing the behavior of neural IR models. Transactions of the Association for Computational Linguistics, 10:224-239, 2022. doi: 10.1162/tacl_a_00457. URL https://aclanthology.org/2022/tacl-1.13.
[50] P. Manakul, A. Liusie, and M. J. Gales. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. arXiv preprint arXiv:2303.08896, 2023.
[51] S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer. Rethinking the role of demonstrations: What makes in-context learning work? In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11048–11064, Abu Dhabi, United Arab Emirates, Dec. 2022. Association for Computational Linguistics. URL https://aclanthology.org/2022.emnlp-main.759
[52] M. Morin and M. Willetts. Non-determinism in tensorflow resnets. arXiv preprint arXiv:2001.11396, 2020.
[53] S. Mysore, T. O' Gorman, A. McCallum, and H. Zamani. Csfcube - a test collection of computer science research articles for faceted query by example. In J. Vanschoren and S. Yeung, editors, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1. Curran, 2021. URL https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/20f07591c6fb220ffe637cda29bb3f6-Paper-round2.pdf
[54] S. Mysore, A. Cohan, and T. Hope. Multi-vector models with textual guidance for fine-grained scientific document similarity. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4453-4470, 2022.
[55] S. Mysore, A. McCallum, and H. Zamani. Large language model augmented narrative driven recommendations, 2023.
[56] M. Neves, D. Butzke, and B. Grune. Evaluation of scientific elements for text similarity in biomedical publications. In Proceedings of the 6th Workshop on Argument Mining, pages 124–135, Florence, Italy, Aug. 2019. Association for Computational Linguistics. doi: 10.18653/v1/W19-4515. URL https://aclanthology.org/W19-4515.
[57] 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, 2640:660, 2016.
[58] M. Ostendorff. Contextual document similarity for content-based literature recommender systems. arXiv preprint arXiv:2008.00202, 2020.
[59] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730-27744, 2022.
[60] C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, and D. Yang. Is chatgpt a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476, 2023.
[61] S. Reddy, D. Chen, and C. D. Manning. Coqa: A conversational question answering challenge. Transactions of the Association for Computational Linguistics, 7:249-266, 2019.
[62] N. Reimers and I. Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3982-3992, 2019.