% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@InProceedings{Bongard.et.al.2022.NLLP,
  title     = {{The Legal Argument Reasoning Task in Civil Procedure}},
  author    = {Bongard, Leonard and Held, Lena and Habernal, Ivan},
  booktitle = {Proceedings of the Natural Legal Language Processing
               Workshop 2022},
  pages     = {194--207},
  year      = {2022},
  address   = {Abu Dhabi, UAE},
  publisher = {Association for Computational Linguistics}
}

@inproceedings{chalkidis-etal-2022-lexglue,
    title = "{L}ex{GLUE}: A Benchmark Dataset for Legal Language Understanding in {E}nglish",
    author = "Chalkidis, Ilias  and
      Jana, Abhik  and
      Hartung, Dirk  and
      Bommarito, Michael  and
      Androutsopoulos, Ion  and
      Katz, Daniel  and
      Aletras, Nikolaos",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.297",
    doi = "10.18653/v1/2022.acl-long.297",
    pages = "4310--4330",
    abstract = "Laws and their interpretations, legal arguments and agreements are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.",
}

@book{glannon2018glannon,
  title={Glannon Guide to Civil Procedure: Learning Civil Procedure Through Multiple-Choice Questions and Analysis},
  author={Glannon, J.W.},
  isbn={9781543805574},
  series={Glannon Guides Series},
  url={https://books.google.co.uk/books?id=NkBvDwAAQBAJ},
  year={2018},
  publisher={Aspen Publishing}
}

@misc{beltagy2020longformer,
      title={Longformer: The Long-Document Transformer}, 
      author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
      year={2020},
      eprint={2004.05150},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{zaheer2021big,
      title={Big Bird: Transformers for Longer Sequences}, 
      author={Manzil Zaheer and Guru Guruganesh and Avinava Dubey and Joshua Ainslie and Chris Alberti and Santiago Ontanon and Philip Pham and Anirudh Ravula and Qifan Wang and Li Yang and Amr Ahmed},
      year={2021},
      eprint={2007.14062},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{lin2018focal,
      title={Focal Loss for Dense Object Detection}, 
      author={Tsung-Yi Lin and Priya Goyal and Ross Girshick and Kaiming He and Piotr Dollár},
      year={2018},
      eprint={1708.02002},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{chalkidis-garneau-etal-2023-lexlms,
    title = {{LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development}},
    author = "Chalkidis*, Ilias and 
              Garneau*, Nicolas and
              Goanta, Catalina and 
              Katz, Daniel Martin and 
              Søgaard, Anders",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics",
    month = july,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2305.07507",
}


@article{Niklaus2023MultiLegalPileA6,
  title={MultiLegalPile: A 689GB Multilingual Legal Corpus},
  author={Joel Niklaus and Veton Matoshi and Matthias Sturmer and Ilias Chalkidis and Daniel E. Ho},
  journal={ArXiv},
  year={2023},
  volume={abs/2306.02069}
}

@misc{chalkidis2020legalbert,
      title={LEGAL-BERT: The Muppets straight out of Law School}, 
      author={Ilias Chalkidis and Manos Fergadiotis and Prodromos Malakasiotis and Nikolaos Aletras and Ion Androutsopoulos},
      year={2020},
      eprint={2010.02559},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{chen2023reconcile,
      title={ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs}, 
      author={Justin Chih-Yao Chen and Swarnadeep Saha and Mohit Bansal},
      year={2023},
      eprint={2309.13007},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{wang-etal-2022-calibrating,
    title = "Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study",
    author = {Wang, Cheng  and
      Balazs, Jorge  and
      Szarvas, Gy{\"o}rgy  and
      Ernst, Patrick  and
      Poddar, Lahari  and
      Danchenko, Pavel},
    editor = "Li, Yunyao  and
      Lazaridou, Angeliki",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-industry.14",
    doi = "10.18653/v1/2022.emnlp-industry.14",
    pages = "145--153",
    abstract = "Imbalanced data distribution is a practical and common challenge in building production-level machine learning (ML) models in industry, where data usually exhibits long-tail distributions. For instance, in virtual AI Assistants, such as Google Assistant, Amazon Alexa and Apple Siri, the {``}play music{''} or {``}set timer{''} utterance is exposed to an order of magnitude more traffic than other skills. This can easily cause trained models to overfit to the majority classes, categories or intents, lead to model miscalibration. The uncalibrated models output unreliable (mostly overconfident) predictions, which are at high risk of affecting downstream decision-making systems. In this work, we study the calibration of production models in the industry use-case of predicting product return reason codes in customer service conversations of an online retail store; The returns reasons also exhibit class imbalance. To alleviate the resulting miscalibration in the production ML model, we streamline the model development and deployment using focal loss (CITATION).We empirically show the effectiveness of model training with focal loss in learning better calibrated models, as compared to standard cross-entropy loss. Better calibration, in turn, enables better control of the precision-recall trade-off for the models deployed in production.",
}

@misc{li2023sailer,
      title={SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval}, 
      author={Haitao Li and Qingyao Ai and Jia Chen and Qian Dong and Yueyue Wu and Yiqun Liu and Chong Chen and Qi Tian},
      year={2023},
      eprint={2304.11370},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

@ARTICLE{10255647,
  author={Cui, Junyun and Shen, Xiaoyu and Wen, Shaochun},
  journal={IEEE Access}, 
  title={A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges}, 
  year={2023},
  volume={11},
  number={},
  pages={102050-102071},
  keywords={Law;Task analysis;Surveys;Benchmark testing;Natural language processing;Measurement;Biological system modeling;Benchmark testing;Neural networks;Legal judgment prediction;natural language processing;survey;benchmark datasets;neural network},
  doi={10.1109/ACCESS.2023.3317083}}

@misc{louis2023interpretable,
      title={Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models}, 
      author={Antoine Louis and Gijs van Dijck and Gerasimos Spanakis},
      year={2023},
      eprint={2309.17050},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{colombo2024saullm7b,
      title={SaulLM-7B: A pioneering Large Language Model for Law}, 
      author={Pierre Colombo and Telmo Pessoa Pires and Malik Boudiaf and Dominic Culver and Rui Melo and Caio Corro and Andre F. T. Martins and Fabrizio Esposito and Vera Lúcia Raposo and Sofia Morgado and Michael Desa},
      year={2024},
      eprint={2403.03883},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{cui2023chatlaw,
      title={ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases}, 
      author={Jiaxi Cui and Zongjian Li and Yang Yan and Bohua Chen and Li Yuan},
      year={2023},
      eprint={2306.16092},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{savelka2023explaining,
      title={Explaining Legal Concepts with Augmented Large Language Models (GPT-4)}, 
      author={Jaromir Savelka and Kevin D. Ashley and Morgan A. Gray and Hannes Westermann and Huihui Xu},
      year={2023},
      eprint={2306.09525},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}