Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909445547622400 |
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| author | Yue, Chongjian Xu, Xinrun Ma, Xiaojun Du, Lun Ding, Zhiming Han, Shi Zhang, Dongmei Zhang, Qi |
| author_facet | Yue, Chongjian Xu, Xinrun Ma, Xiaojun Du, Lun Ding, Zhiming Han, Shi Zhang, Dongmei Zhang, Qi |
| contents | Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text, containing textual and tabular data, remains unexplored. The hybrid text often appears in the form of hybrid long documents (HLDs), which far exceed the token limit of LLMs. Consequently, we apply an Automated Information Extraction framework (AIE) to enable LLMs to process the HLDs and carry out experiments to analyse four important aspects of information extraction from HLDs. Given the findings: 1) The effective way to select and summarize the useful part of a HLD. 2) An easy table serialization way is enough for LLMs to understand tables. 3) The naive AIE has adaptability in many complex scenarios. 4) The useful prompt engineering to enhance LLMs on HLDs. To address the issue of dataset scarcity in HLDs and support future work, we also propose the Financial Reports Numerical Extraction (FINE) dataset. The dataset and code are publicly available in the attachments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset Yue, Chongjian Xu, Xinrun Ma, Xiaojun Du, Lun Ding, Zhiming Han, Shi Zhang, Dongmei Zhang, Qi Computation and Language Artificial Intelligence Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text, containing textual and tabular data, remains unexplored. The hybrid text often appears in the form of hybrid long documents (HLDs), which far exceed the token limit of LLMs. Consequently, we apply an Automated Information Extraction framework (AIE) to enable LLMs to process the HLDs and carry out experiments to analyse four important aspects of information extraction from HLDs. Given the findings: 1) The effective way to select and summarize the useful part of a HLD. 2) An easy table serialization way is enough for LLMs to understand tables. 3) The naive AIE has adaptability in many complex scenarios. 4) The useful prompt engineering to enhance LLMs on HLDs. To address the issue of dataset scarcity in HLDs and support future work, we also propose the Financial Reports Numerical Extraction (FINE) dataset. The dataset and code are publicly available in the attachments. |
| title | Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.20072 |