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Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering.

, , , , and . CoNLL, page 44-54. Association for Computational Linguistics, (2019)

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NIST TAC SM-KBP 2018 System Description: JHU/UR Pipeline., , , , , , , , and . TAC, NIST, (2018)The Factuality of Large Language Models in the Legal Domain., , , , and . CoRR, (2024)BLT: Can Large Language Models Handle Basic Legal Text?, , and . CoRR, (2023)Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations., , , , and . CoRR, (2024)NIST TAC SM-KBP 2019 System Description: JHU/UR Framework., , , , , , , , , and 5 other author(s). TAC, NIST, (2019)A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering., , and . NLLP@KDD, volume 2645 of CEUR Workshop Proceedings, page 31-38. CEUR-WS.org, (2020)Can GPT-3 Perform Statutory Reasoning?, , and . ICAIL, page 22-31. ACM, (2023)CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation., , , , , , , and . CoRR, (2024)Learning from Multiview Correlations in Open-Domain Videos., , , , and . CoRR, (2018)Factoring Statutory Reasoning as Language Understanding Challenges., and . ACL/IJCNLP (1), page 2742-2758. Association for Computational Linguistics, (2021)