GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models (2024.naacl-long)
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| Challenge: | Existing relation extraction methods rely on exact matching with human-annotated reference relations, while GRE methods produce diverse and semantically accurate relations. |
| Approach: | They propose a multi-dimensional assessment of relation extraction methods using human-annotated reference relations. |
| Outcome: | The proposed method is consistent with human preferences for RE quality. |
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| Challenge: | Existing methods for relation extraction (RE) are discriminative and generative . previous studies show that discriminative models can support generative RE . |
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
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| Challenge: | Standard supervised approaches to RE learn to tag tokens comprising entity spans and then predict the relationship between them. |
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| Challenge: | Relation Extraction (RE) is a critical step in information extraction due to its wide-scale applicability for downstream applications such as Knowledge Base creation and Question Answering (QA). |
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| Challenge: | Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks. |
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