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Exploring the Role of BERT Token Representations to Explain Sentence Probing Results.

, , и . EMNLP (1), стр. 792-806. Association for Computational Linguistics, (2021)

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Diana Modarressi-Tehrani

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Exploring the Role of BERT Token Representations to Explain Sentence Probing Results., , и . EMNLP (1), стр. 792-806. Association for Computational Linguistics, (2021)AdapLeR: Speeding up Inference by Adaptive Length Reduction., , и . ACL (1), стр. 1-15. Association for Computational Linguistics, (2022)Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities., , и . EACL, стр. 1946-1951. Association for Computational Linguistics, (2023)MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory., , , , и . CoRR, (2024)RET-LLM: Towards a General Read-Write Memory for Large Language Models., , , и . CoRR, (2023)GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers., , , и . NAACL-HLT, стр. 258-271. Association for Computational Linguistics, (2022)Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids' Representations., , , , и . BlackboxNLP@EMNLP, стр. 375-388. Association for Computational Linguistics, (2021)BERT on a Data Diet: Finding Important Examples by Gradient-Based Pruning., , , , , и . CoRR, (2022)DecompX: Explaining Transformers Decisions by Propagating Token Decomposition., , , , и . ACL (1), стр. 2649-2664. Association for Computational Linguistics, (2023)