1. Bilgin, O., Fields, L., Jr., A. L., Marji, Z., Nighojkar, A., Steinle, S., & Licato, J. (2023) Amhr lab 2023 coliee competition approach. In Workshop of the tenth competition on legal information extraction/entailment (COLIEE’2023) in the 19th international conference on artificial intelligence and law (ICAIL)
2. Bui, Q. M., Do, D. T., Le, N. K., Nguyen, D. H., Nguyen, K. V. H., Anh, T. P. N., & Nguyen, M. L. (2023). Jnlp $$@$$coliee-2023: Data argumentation and large language model for legal case retrieval and entailment. In Workshop of the tenth competition on legal information extraction/entailment (COLIEE’2023) in the 19th international conference on artificial intelligence and law (ICAIL)
3. Custeau, M., & Inkpen, D. (2023). Individual models can perform better than agreement-based ensembles. In Workshop of the tenth competition on legal information extraction/entailment (COLIEE’2023) in the 19th international conference on artificial intelligence and law (ICAIL)
4. Debbarma, R., Prawar, P., Chakraborty, A., & Bedathur, S. (2023) Iitdli: Legal case retrieval based on lexical models. In Workshop of the tenth competition on legal information extraction/entailment (COLIEE’2023) in the 19th international conference on artificial intelligence and law (ICAIL)
5. Li, H., Su, W., Wang, C., Wu, Y., Ai, Q., & Liu, Y. (2023). Thuir$$@$$coliee 2023: Incorporating structural knowledge into pre-trained language models for legal case retrieval. In Workshop of the tenth competition on legal information extraction/entailment (COLIEE’2023) in the 19th international conference on artificial intelligence and law (ICAIL)