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We introduce two novel methods, Tree-Search and Self-contextualizing QA, designed to enhance the performance of large language models (LLMs) in question-answering tasks.
Yang Z., Dai Z., Yang Y., Carbonell J. G., Salakhutdinov R., Le Q. V., 2019, CoRR, abs/1906.08237
1906
Earlier work this paper cites.
Chen D., Fisch A., Weston J., Bordes A., 2017, in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, pp 1870–1879, doi:10.18653/v1/P17-1171 , https://aclanthology.org/P17-1171
2017
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Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser L. u., Polosukhin I., 2017, in Guyon I., Luxburg U. V., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R., eds, Vol. 30, Advances in Neural Information Processing Systems. Curran Associates, Inc., https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
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Thorne J., Vlachos A., Christodoulopoulos C., Mittal A., 2018, in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, Louisiana, pp 809–819, doi:10.18653/v1/N18-1074 , https://aclanthology.org/N18-1074
2018
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Devlin J., Chang M.-W., Lee K., Toutanova K., 2019, in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186, doi:10.18653/v1/N19-1423 , https://aclanthology.org/N19-1423
2019
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Brown T., et al., 2020, in Larochelle H., Ranzato M., Hadsell R., Balcan M., Lin H., eds, Vol. 33, Advances in Neural Information Processing Systems. Curran Associates, Inc., pp 1877–1901, https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
2020
Cited alongside, same era.
Lewis M., Liu Y., Goyal N., Ghazvininejad M., Mohamed A., Levy O., Stoyanov V., Zettlemoyer L., 2020, in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 7871–7880, doi:10.18653/v1/2020.acl-main.703 , https://aclanthology.org/2020.acl-main.703
2020
Cited alongside, same era.
Raffel C., et al., 2020, J. Mach. Learn. Res., 21
2020
Cited alongside, same era.
Roberts A., Raffel C., Shazeer N., 2020, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 5418–5426, doi:10.18653/v1/2020.emnlp-main.437 , https://aclanthology.org/2020.emnlp-main.437
Sanh V., et al., 2022, in International Conference on Learning Representations. https://openreview.net/forum?id=9Vrb9D0WI4
2022
Later among the works it cites.
2023
Closest in time.
Huang F., Kwak H., An J., 2023, in Companion Proceedings of the ACM Web Conference 2023. ACM, doi:10.1145/3543873.3587368 , https://doi.org/10.1145%2F3543873.3587368
2023
Closest in time.
Touvron H., et al., 2023, LLaMA: Open and Efficient Foundation Language Models ( arXiv:2302.13971 )
2023
Closest in time.
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2020
Cited alongside, same era.