3260 papers • 126 benchmarks • 313 datasets
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An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both.
This work introduces WikiCREM (Wikipedia CoREferences Masked) a large-scale, yet accurate dataset of pronoun disambiguation instances, and uses a language-model-based approach for pronoun resolution in combination with this dataset, beating previous state-of-the-art approaches on 6 out of 7 datasets.
The best method achieves an accuracy between 92% and 100% in detecting if an LLM is contaminated with seven datasets, containing train and test/validation partitions, when contrasted with manual evaluation by human experts.
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