Year: 2025
Type: article
Abstract: With the growing demand for labeled textual data in Natural Language Processing (NLP), traditional data collection and annotation methods face significant challenges, such as high cost, limited scalability, and privacy constraints. This study presents a novel web-based platform that automates text data generation and labeling by integrating Llama 3.3, an open-source large language model (LLM), with advanced prompt engineering techniques. A core contribution of this work is the Attributed Prompt Engineering Framework, which enables modular and configurable prompt templates for both data generation and labeling tasks. This framework combines zero-shot, few-shot, role-based, and chain-of-thought prompting strategies within a unified architecture to optimize output quality and control. Users can interactively configure prompt parameters and generate synthetic datasets or annotate raw data with minimal human intervention. We evaluated the platform using both benchmark datasets (AG News, Yelp, Amazon Reviews) and two fully synthetic datasets we generated (restaurant reviews and news articles). The system achieved 99% accuracy and F1-score on generated news article data, 98% accuracy and F1-score on generated restaurant review data, and 92%, 90%, and 89% accuracy and F1-scores on the benchmark labeling tasks for AG News, Yelp Reviews, and Amazon Reviews, respectively, demonstrating high effectiveness and generalizability. A usability study also confirmed the platform’s practicality for non-expert users. This work advances scalable NLP data pipeline design and provides a cost-effective alternative to manual annotation for supervised learning applications.
Source: Electronics
Institution: King Saud University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: King Saud University
Award: G6305580809
OpenAlex ID: W4412198857