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Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
March 26, 2024, 4:51 a.m. | Wenhao Huang, Qianyu He, Zhixu Li, Jiaqing Liang, Yanghua Xiao
cs.CL updates on arXiv.org arxiv.org
Abstract: Definition bias is a negative phenomenon that can mislead models. Definition bias in information extraction appears not only across datasets from different domains but also within datasets sharing the same domain. We identify two types of definition bias in IE: bias among information extraction datasets and bias between information extraction datasets and instruction tuning datasets. To systematically investigate definition bias, we conduct three probing experiments to quantitatively analyze it and discover the limitations of unified …
arxiv biases cs.cl definition extraction information information extraction type
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