@inproceedings{huang-etal-2024-one,
    title = "Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases",
    author = "Huang, Wenhao  and
      He, Qianyu  and
      Li, Zhixu  and
      Liang, Jiaqing  and
      Xiao, Yanghua",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-emnlp.601/",
    doi = "10.18653/v1/2024.findings-emnlp.601",
    pages = "10274--10287",
    abstract = "Definition bias is a negative phenomenon that can mislead models. However, 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."
}