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SMoP: Towards Efficient and Effective Prompt Tuning with Sparse Mixture-of-Prompts.

, , , , and . EMNLP, page 14306-14316. Association for Computational Linguistics, (2023)

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Client-Customized Adaptation for Parameter-Efficient Federated Learning., , , , and . ACL (Findings), page 1159-1172. Association for Computational Linguistics, (2023)SMoP: Towards Efficient and Effective Prompt Tuning with Sparse Mixture-of-Prompts., , , , and . EMNLP, page 14306-14316. Association for Computational Linguistics, (2023)Mitigating Annotation Artifacts in Physical Commonsense Reasoning Benchmark., and . IEEE Big Data, page 6236-6238. IEEE, (2023)Improving Bias Mitigation through Bias Experts in Natural Language Understanding., , , , , and . EMNLP, page 11053-11066. Association for Computational Linguistics, (2023)Tutoring Helps Students Learn Better: Improving Knowledge Distillation for BERT with Tutor Network., , , , , and . EMNLP, page 7371-7382. Association for Computational Linguistics, (2022)