The content discusses the challenges of processing Hybrid Long Documents (HLDs) containing both textual and tabular data. It introduces the AIE framework with four modules: Segmentation, Retrieval, Summarization, and Extraction. The experiments conducted on the Financial Reports Numerical Extraction (FINE) dataset demonstrate the effectiveness of AIE in handling HLDs.
Large Language Models (LLMs) have shown proficiency in various natural language tasks but face limitations in comprehending hybrid text like HLDs. The study explores the adaptability of LLMs for extracting information from HLDs through the AIE framework. Various strategies such as table serialization formats, retrieval quantities, summarization techniques, numerical precision enhancement, keyword completion, and shot numbers are analyzed for their impact on information extraction accuracy.
The results indicate that AIE significantly improves LLMs' ability to handle HLDs across different domains like financial reports, scientific papers, and Wikipedia articles. Limitations include model ability constraints and cost considerations. Further research is needed to evaluate LLM capabilities in other aspects beyond information extraction.
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by Chongjian Yu... às arxiv.org 03-08-2024
https://arxiv.org/pdf/2305.16344.pdfPerguntas Mais Profundas