An Analytical Study on Toy Age Grading Methods: A Comparative Study of Large Language Models and Machine Learning Approaches

This research delves into the utilization of Large Language Models (LLMs) and Machine Learning (ML) in age grading assessments within the toy industry. It compares the efficacy of these methodologies in establishing suitable age ranges for toys, a critical aspect for ensuring child safety and compliance with regulations. LLMs, harnessing pretrained models, enable swift evaluations through visual and textual inputs, yielding instant results with minimal human intervention. Conversely, ML models necessitate training on labeled datasets to gauge age appropriateness, presenting opportunities for ongoing improvement with updated data. A comparative analysis between a trained ML model and GPT 3.5 Turbo unveiled differ accuracies and readiness in age grading assessments. While LLMs offer rapid insights, ML models shine in scalability and the refinement of accuracy over time. The study's findings highlight the strengths and limitations of each approach, proposing customized applications for stakeholders in the toy safety and development domain based on their distinct requirements and resources.