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Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework., , , , , и . ICLR, OpenReview.net, (2020)ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models., , , , , , и . CoRR, (2024)Enhancing Factual Consistency of Abstractive Summarization., , , , , , и . NAACL-HLT, стр. 718-733. Association for Computational Linguistics, (2021)Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort., , , , , , и . COLING, стр. 70-82. Association for Computational Linguistics, (2018)Experiments in Curation: Towards Machine-Assisted Construction of Software Architecture Knowledge Bases., , , , и . ICSA, стр. 79-88. IEEE Computer Society, (2017)Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization., , , , , , , , , и 2 other автор(ы). ACL (1), стр. 5095-5112. Association for Computational Linguistics, (2023)G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment., , , , , и . EMNLP, стр. 2511-2522. Association for Computational Linguistics, (2023)Rho-1: Not All Tokens Are What You Need., , , , , , , , , и 1 other автор(ы). CoRR, (2024)OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understanding., , , , , , , , , и . CoRR, (2024)A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining., , , и . EMNLP (Findings), том EMNLP 2020 из Findings of ACL, стр. 194-203. Association for Computational Linguistics, (2020)