ChunkUIE: Chunked instruction-based unified information extraction.
Large language models (LLMs) have demonstrated remarkable performance across various linguistic tasks. However, existing LLMs perform inadequately in information extraction tasks for both Chinese and English. Numerous studies attempt to enhance model performance by increasing the scale of training d...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326764 |
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| author | Wei Li Yingzhen Liu Yinling Yang Ting Zhang Wei Men |
| author_facet | Wei Li Yingzhen Liu Yinling Yang Ting Zhang Wei Men |
| author_sort | Wei Li |
| collection | DOAJ |
| description | Large language models (LLMs) have demonstrated remarkable performance across various linguistic tasks. However, existing LLMs perform inadequately in information extraction tasks for both Chinese and English. Numerous studies attempt to enhance model performance by increasing the scale of training data. However, discrepancies in the number and type of schemas used during training and evaluation can harm model effectiveness. To tackle this challenge, we propose ChunkUIE, a unified information extraction model that supports Chinese and English. We design a chunked instruction construction strategy that randomly and reproducibly divides all schemas into chunks containing an identical number of schemas. This approach ensures that the union of schemas across all chunks encompasses all schemas. By limiting the number of schemas in each instruction, this strategy effectively addresses the performance degradation caused by inconsistencies in schema counts between training and evaluation. Additionally, we construct some challenging negative schemas using a predefined hard schema dictionary, which mitigates the model's semantic confusion regarding similar schemas. Experimental results demonstrate that ChunkUIE enhances zero-shot performance in information extraction. |
| format | Article |
| id | doaj-art-54be924bf664493389aafa53c01e970b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-54be924bf664493389aafa53c01e970b2025-08-20T03:50:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032676410.1371/journal.pone.0326764ChunkUIE: Chunked instruction-based unified information extraction.Wei LiYingzhen LiuYinling YangTing ZhangWei MenLarge language models (LLMs) have demonstrated remarkable performance across various linguistic tasks. However, existing LLMs perform inadequately in information extraction tasks for both Chinese and English. Numerous studies attempt to enhance model performance by increasing the scale of training data. However, discrepancies in the number and type of schemas used during training and evaluation can harm model effectiveness. To tackle this challenge, we propose ChunkUIE, a unified information extraction model that supports Chinese and English. We design a chunked instruction construction strategy that randomly and reproducibly divides all schemas into chunks containing an identical number of schemas. This approach ensures that the union of schemas across all chunks encompasses all schemas. By limiting the number of schemas in each instruction, this strategy effectively addresses the performance degradation caused by inconsistencies in schema counts between training and evaluation. Additionally, we construct some challenging negative schemas using a predefined hard schema dictionary, which mitigates the model's semantic confusion regarding similar schemas. Experimental results demonstrate that ChunkUIE enhances zero-shot performance in information extraction.https://doi.org/10.1371/journal.pone.0326764 |
| spellingShingle | Wei Li Yingzhen Liu Yinling Yang Ting Zhang Wei Men ChunkUIE: Chunked instruction-based unified information extraction. PLoS ONE |
| title | ChunkUIE: Chunked instruction-based unified information extraction. |
| title_full | ChunkUIE: Chunked instruction-based unified information extraction. |
| title_fullStr | ChunkUIE: Chunked instruction-based unified information extraction. |
| title_full_unstemmed | ChunkUIE: Chunked instruction-based unified information extraction. |
| title_short | ChunkUIE: Chunked instruction-based unified information extraction. |
| title_sort | chunkuie chunked instruction based unified information extraction |
| url | https://doi.org/10.1371/journal.pone.0326764 |
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