This study presents a comparative evaluation of ten state-of-the-art large language models (LLMs) applied to unstructured text categorization using the Interactive Advertising Bureau (IAB) 2.2 hierarchical taxonomy. The analysis employed a uniform dataset of 8,660 human-annotated samples and identical zero-shot prompts to ensure methodological consistency across all models. Evaluation metrics included four classic measures - accuracy, precision, recall, and F1-score - and three LLM-specific indicators: hallucination ratio, inflation ratio, and categorization cost. Results show that, despite their rapid advancement, contemporary LLMs achieve only moderate classic performance, with average scores of 34% accuracy, 42% precision, 45% recall, and 41% F1-score. Hallucination and inflation ratios reveal that models frequently overproduce categories relative to human annotators. Among the evaluated systems, Gemini 1.5/2.0 Flash and GPT 20B/120B offered the most favorable cost-to-performance balance, while GPT 120B demonstrated the lowest hallucination ratio. The findings suggest that scaling and architectural improvements alone do not ensure better categorization accuracy, as the task requires compressing rich unstructured text into a limited taxonomy - a process that challenges current model architectures. To address these limitations, a separate ensemble-based approach was developed and tested. The ensemble method, in which multiple LLMs act as independent experts, substantially improved accuracy, reduced inflation, and completely eliminated hallucinations. These results indicate that coordinated orchestration of models - rather than sheer scale - may represent the most effective path toward achieving or surpassing human-expert performance in large-scale text categorization.

The Interactive Advertising Bureau (IAB), the national trade association for the digital media and marketing industries, is focusing its AI Standards Working Group to develop artificial intelligence (AI) standards, best practices, use cases, and terminologies in an effort to scale AI and enable the industry on its full potential. The group is newly co-chaired by IBM Watson Advertising and Nielsen. The first release of 2021, "Artificial Intelligence Use Cases and Best Practices for Marketing," will help executive leaders, marketers, and technologists get the most from AI, and do it responsibly. Created for those already working with AI or looking to leverage it in their business, this guide draws directly from the real-world experience of co-chairs IBM Watson Advertising and Nielsen as well as top publishers, agencies, and ad tech companies in the industry. It's not an ivory tower overview of AI: it's a specific guide for what to do for executives that are in the thick of it.