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Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets

, , and . (2019)cite arxiv:1908.07898Comment: EMNLP-IJCNLP 2019.

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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, , , , , , , , , and 441 other author(s). (2022)cite arxiv:2206.04615Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench.A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains., , , , , , , , and . CoRR, (2024)Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions., , , and . EACL, page 1771-1781. Association for Computational Linguistics, (2023)Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets., , and . EMNLP/IJCNLP (1), page 1161-1166. Association for Computational Linguistics, (2019)SCROLLS: Standardized CompaRison Over Long Language Sequences., , , , , , , , , and 1 other author(s). EMNLP, page 12007-12021. Association for Computational Linguistics, (2022)Evaluating Semantic Parsing against a Simple Web-based Question Answering Model., , and . *SEM, page 161-167. Association for Computational Linguistics, (2017)Do Large Language Models Latently Perform Multi-Hop Reasoning?, , , , and . CoRR, (2024)Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies., , , , , and . CoRR, (2021)Inferring Implicit Relations with Language Models., , and . CoRR, (2022)Backward Lens: Projecting Language Model Gradients into the Vocabulary Space., , , and . CoRR, (2024)