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Estimating Training Data Influence by Tracking Gradient Descent., , , and . CoRR, (2020)Attention-based Multimodal Neural Machine Translation., , , , and . WMT, page 639-645. The Association for Computer Linguistics, (2016)EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks., , , and . CoRR, (2021)Towards Tracing Knowledge in Language Models Back to the Training Data., , , , , , and . EMNLP (Findings), page 2429-2446. Association for Computational Linguistics, (2022)Estimating Training Data Influence by Tracing Gradient Descent., , , and . NeurIPS, (2020)"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output., , , , , , and . CHI Extended Abstracts, page 10:1-10:9. ACM, (2024)FAVOR#: Sharp Attention Kernel Approximations via New Classes of Positive Random Features., , , , , and . CoRR, (2023)First is Better Than Last for Training Data Influence., , , , and . CoRR, (2022)The Penalty Imposed by Ablated Data Augmentation., , and . CoRR, (2020)First is Better Than Last for Language Data Influence., , , , and . NeurIPS, (2022)