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StarCoder: may the source be with you!, , , , , , , , , and 57 other author(s). (2023)cite arxiv:2305.06161.Dated Data: Tracing Knowledge Cutoffs in Large Language Models., , , , , and . CoRR, (2024)Äccording to . . . ": Prompting Language Models Improves Quoting from Pre-Training Data., , , , , and . EACL (1), page 2288-2301. Association for Computational Linguistics, (2024)Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction., , , , , , , , , and 3 other author(s). EMNLP (1), page 1950-1967. Association for Computational Linguistics, (2021)StarCoder 2 and The Stack v2: The Next Generation., , , , , , , , , and 47 other author(s). CoRR, (2024)Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data., , , , and . CoRR, (2024)StarCoder: may the source be with you!, , , , , , , , , and 57 other author(s). CoRR, (2023)Data Portraits: Recording Foundation Model Training Data., and . CoRR, (2023)Äccording to ..." Prompting Language Models Improves Quoting from Pre-Training Data., , , , , and . CoRR, (2023)Pretrained Models for Multilingual Federated Learning., , , , and . NAACL-HLT, page 1413-1421. Association for Computational Linguistics, (2022)