In the public procurement domain, extracting accurate tender entities from unstructured text remains a critical, less explored challenge, because tender data is highly sensitive and confidential, and not available openly. Previously, state-of-the-art NLP models were developed for this task; however developing an NER model from scratch required huge amounts of data and resources. Similarly, performing fine-tuning of a transformer-based model like BERT requires training data, as a result posing challenges in training data cost, model generalization, and data privacy. To address these challenges, an emerging LLM such as GPT-4 in a Few-shot learning environment achieves SOTA performance comparable to fine-tuned models. However, being dependent on the closed-source commercial LLMs involves high cost and privacy concerns. In this study, we have investigated open-source LLMs like Mistral and LLAMA-3, focusing on the tender domain for the NER tasks on local consumer-grade CPUs in three different environments: Zero-shot, One-shot, and Few-shot learning. The motivation is to efficiently lessen costs compared to a cloud solution while preserving accuracy and data privacy. Similarly, we have utilized two datasets open-source from Singapore and closed-source commercially sensitive data provided by Siemens. As a result, all the open-source LLMs achieve above 85% F1-score on an open-source dataset and above 90% F1-score on a closed-source dataset.

Extracting structured text from complex tables in PDF tender documents remains a challenging task due to the loss of structural and positional information during the extraction process. AI-based models often require extensive training data, making development from scratch both tedious and time-consuming. Our research focuses on identifying tender entities in complex table formats within PDF documents. To address this, we propose a novel approach utilizing few-shot learning with large language models (LLMs) to restore the structure of extracted text. Additionally, handcrafted rules and regular expressions are employed for precise entity classification. To evaluate the robustness of LLMs with few-shot learning, we employ data-shuffling techniques. Our experiments show that current text extraction tools fail to deliver satisfactory results for complex table structures. However, the few-shot learning approach significantly enhances the structural integrity of extracted data and improves the accuracy of tender entity identification.