Papers with unified information extraction
General Collaborative Framework between Large Language Model and Experts for Universal Information Extraction (2024.findings-emnlp)
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| Challenge: | Existing unified information extraction approaches face challenges such as noise interference, abstract label semantics, and diverse span granularity. |
| Approach: | They propose a general Collaborative Information Extraction framework to address these challenges in universal information extraction tasks. |
| Outcome: | The proposed framework is based on a general Recognizer and task-specific Experts for recognizing predefined types and extracting spans respectively. |
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases (2024.findings-emnlp)
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| Challenge: | Definition bias is a negative phenomenon that can mislead models. |
| Approach: | They propose a framework that measures definition bias, bias-aware fine-tuning and task-specific bias mitigation to mitigate definition bias in information extraction. |
| Outcome: | The proposed framework mitigates definition bias in information extraction tasks by measuring definition bias, bias-aware fine-tuning, and task-specific bias mitigation. |