The study explores the use of large language models for extracting named entities and correcting spelling errors in text data. Results show that fine-tuned language models can outperform traditional methods in these tasks.
Language models like BERT are utilized for Named Entity extraction from OCR text. The study focuses on NE categories like shop names, addresses, dates, etc. Large Language Models (LLMs) are hypothesized to generatively extract NEs and correct spelling errors simultaneously. Fine-tuned LLMs show potential in improving NE extraction accuracy compared to BERT LMs. The best-performing LLM demonstrates the ability to correct OCR errors in some cases. Different approaches for supervised fine-tuning of LLMs are explored, with a focus on LoRA method optimization. Model formulation as question answering by LM text completion is detailed. The prompt format used for fine-tuning is crucial for optimal performance. Evaluation criteria include precision, recall, and weighted F-measure calculations for each NE category. Results indicate that LLMs can offer competitive performance compared to BERT LMs in NE extraction tasks.
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arxiv.org
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by Edward Whitt... ב- arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00528.pdfשאלות מעמיקות