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Estimating Training Data Influence by Tracking Gradient Descent., , , and . CoRR, (2020)EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks., , , and . CoRR, (2021)Attention-based Multimodal Neural Machine Translation., , , , and . WMT, page 639-645. The Association for Computer Linguistics, (2016)Estimating Training Data Influence by Tracing Gradient Descent., , , and . NeurIPS, (2020)Towards Tracing Knowledge in Language Models Back to the Training Data., , , , , , and . EMNLP (Findings), page 2429-2446. Association for Computational Linguistics, (2022)"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output., , , , , , and . CHI Extended Abstracts, page 10:1-10:9. ACM, (2024)Chefs' Random Tables: Non-Trigonometric Random Features., , , , , and . CoRR, (2022)Analyzing Privacy Policies at Scale: From Crowdsourcing to Automated Annotations., , , , , , , , , and 1 other author(s). ACM Trans. Web, 13 (1): 1:1-1:29 (2019)Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning., , , , , and . CoRR, (2023)UT5: Pretraining Non autoregressive T5 with unrolled denoising., , , and . CoRR, (2023)