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.
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信息提取中的定义偏见是一种负面现象，可能会误导模型。为了系统地调查和解决信息提取中的定义偏见，我们提出了一个多阶段框架，包括定义偏见测量、偏见感知微调和任务特定的偏见缓解。实验证明了我们框架在解决定义偏见方面的有效性。