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Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios.
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Kano, Y., Kim, M., Goebel, R., Satoh, K.: Overview of COLIEE 2017. In: COLIEE 2017 (EPiC Series in Computing, vol. 47). pp. 1–8 (2017)
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Kano, Y., Kim, M.Y., Yoshioka, M., Lu, Y., Rabelo, J., Kiyota, N., Goebel, R., Satoh, K.: COLIEE-2018: Evaluation of the competition on legal information extraction and entailment. In: JSAI International Symposium on Artificial Intelligence. pp. 177–192 (2018)
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Rabelo, J., Kim, M.Y., Goebel, R., Yoshioka, M., Kano, Y., Satoh, K.: A summary of the COLIEE 2019 competition. In: JSAI International Symposium on Artificial Intelligence. pp. 34–49 (2019)
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 1877–1901. Curran Associates, Inc. (2020), https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
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Carmo, D., Piau, M., Campiotti, I., Nogueira, R., Lotufo, R.: Ptt5: Pretraining and validating the t5 model on brazilian portuguese data (2020)
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Nogueira, R., Jiang, Z., Pradeep, R., Lin, J.: Document ranking with a pretrained sequence-to-sequence model. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. pp. 708–718 (2020)
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Pradeep, R., Ma, X., Zhang, X., Cui, H., Xu, R., Nogueira, R., Lin, J.: H2oloo at trec 2020: When all you got is a hammer… deep learning, health misinformation, and precision medicine. Corpus 5
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Rabelo, J., Kim, M., Goebel, R.: Application of text entailment techniques in coliee 2020. International Workshop on Juris-informatics (JURISIN) associated with JSAI International Symposia on AI (JSAI-isAI) (2020)
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Rabelo, J., Kim, M.Y., Goebel, R., Yoshioka, M., Kano, Y., Satoh, K.: Coliee 2020: methods for legal document retrieval and entailment. In: JSAI International Symposium on Artificial Intelligence. pp. 196–210. Springer (2020)
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Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21
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Kim, M., Rabelo, J., Goebel, R.: Bm25 and transformer-based legal information extraction and entailment. Proceedings of the COLIEE Workshop in ICAIL (2021)
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Rosa, G.M., Rodrigues, R.C., , Nogueira, R., Lotufo, R.: Yes, bm25 is a strong baseline for legal case retrieval. Proceedings of the Eighth International Competition on Legal Information Extraction/Entailment (2021)
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Ratnayaka, G., de Silva, N., Perera, A.S., Pathirana, R.: Effective approach to develop a sentiment annotator for legal domain in a low resource setting. In Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation, pages 252–260, Hanoi, Vietnam. Association for Computational Linguistics. (2020)
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Zhang, E., Gupta, N., Nogueira, R., Cho, K., Lin, J.: Rapidly deploying a neural search engine for the covid-19 open research dataset. In: Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 (2020)
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Rosa, G.M., Rodrigues, R.C., Lotufo, R., Nogueira, R.: To tune or not to tune? zero-shot models for legal case entailment. ICAIL’21, Eighteenth International Conference on Artificial Intelligence and Law, June 21–25, 2021, São Paulo, Brazil (2021)
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2022
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Rabelo, J., Goebel, R., Kim, M.Y., Kano, Y., Yoshioka, M., Satoh, K.: Overview and discussion of the competition on legal information extraction/entailment (coliee) 2021. Rev Socionetwork Strat (2022). https://doi.org/10.1007/s12626-022-00105-z (2022)
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Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A.W., Lester, B., Du, N., Dai, A.M., Le, Q.V.: Finetuned language models are zero-shot learners. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=gEZrGCozdqR
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