Is There a One-Model-Fits-All Approach to Information Extraction?
  Revisiting Task Definition Biases
Wenhao Huang Qianyu He Zhixu Li Jiaqing Liang Yanghua Xiao
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 information extraction and large language models in solving definition
bias. To mitigate definition bias in information extraction, we propose a
multi-stage framework consisting of definition bias measurement, bias-aware
fine-tuning, and task-specific bias mitigation. Experimental results
demonstrate the effectiveness of our framework in addressing definition bias.
Resources of this paper can be found at
https://github.com/EZ-hwh/definition-bias