Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case

2026-07-29

AI Summary

This work addresses the challenges of scarce labeled data and weakly expressed, low-salience product attributes in applications such as digital product passports by proposing a two-step verification generative information extraction framework that integrates pretrained language models (PLMs) with large language models (LLMs). The approach first employs a PLM for initial candidate extraction and then leverages a locally deployable open-source LLM—such as those in the Llama family—for secondary verification and error correction, substantially improving extraction accuracy for sparse and weakly expressed entities. Experimental results demonstrate that the proposed framework enhances generalization capability and enables medium-scale models to approach the performance of much larger models, all while preserving data privacy and maintaining computational efficiency. The method has been successfully integrated into a demonstration system tailored for digital product passports.

Abstract

The ability of large language models (LLMs) to process and generate text has introduced potential for applications in information extraction (IE). While it's debated whether LLMs outperform smaller fine-tuned models for classification tasks, their strong generalization capability makes them promising for domains with limited labeled data available for fine-tuning. This advantage is particularly relevant for the emerging application of the digital product passport (DPP), where the problem space is broad but domain-specific data remains scarce. Motivated by this use case, we apply generative IE to the product domain, explicitly addressing efficiency, generalizability, and data privacy constraints. We propose a two-step validation method that integrates a PLM block into the generative IE pipeline and thereby leverages LLMs' correction capability. We discover that such a validation task enhances LLM performance, particularly on the extraction of weakly expressed, low-salience entities that appear sparsely throughout the text. For certain entities, the performance of mid-size models can even reach levels comparable to larger models, and the improvement of first-step PLM predictions also enhance the final LLM output. Nevertheless, the effects on the smallest open-source LLMs (e.g., Llama-3.2 3B) is limited. Based on the findings, we develop a demo application for product information extraction that utilizes locally deployed LLMs, targeting further adaptations to real-world DPP use cases.

Problem

• information extraction
• digital product passport
• product attribute
• low-resource domain
• large language models

Innovation

• generative information extraction
• two-step validation
• large language models
• product attribute extraction
• digital product passport