Xueqiang Xu, Jinfeng Xiao, James Barry, Mohab Elkaref, Jiaru Zou, Pengcheng Jiang, Yunyi Zhang, Maxwell J Giammona, Geeth De Mel, Jiawei Han
| Challenge: | Existing methods based on large language models (LLMs) rely heavily on predefined entity attribute schemas or annotated datasets, often leading to incomplete extraction results. |
| Approach: | They propose a novel approach to entity structure extraction that does not require any schema or annotated datasets. |
| Outcome: | Experiments show that ZOES improves LLMs’ ability to extract more complete entity structures across three different domains, showcasing both the effectiveness and generalizability of the method. |
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