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Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better.

, , , and . CoRR, (2022)

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Neurons in Large Language Models: Dead, N-gram, Positional., , and . CoRR, (2023)BPE-Dropout: Simple and Effective Subword Regularization., , and . ACL, page 1882-1892. Association for Computational Linguistics, (2020)Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation., , and . ACL/IJCNLP (1), page 1126-1140. Association for Computational Linguistics, (2021)HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation., , , , , , , , and . EMNLP, page 638-653. Association for Computational Linguistics, (2023)Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation., , and . CoRR, (2020)BPE-Dropout: Simple and Effective Subword Regularization., , and . CoRR, (2019)Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better., , , and . ACL (1), page 36-50. Association for Computational Linguistics, (2023)Information-Theoretic Probing with Minimum Description Length., and . EMNLP (1), page 183-196. Association for Computational Linguistics, (2020)Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better., , , and . CoRR, (2022)Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation., , and . EACL, page 1059-1075. Association for Computational Linguistics, (2023)