JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry

• URL: http://arxiv.org/abs/2504.20849v1
• Date: Tue, 29 Apr 2025 15:19:06 GMT
• Title: JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry
• Authors: Anum Afzal, Alexandre Mercier, Florian Matthes,
• Abstract summary: This paper investigates data-to-text approaches to automatically generate marketing texts.<n>We leverage Language Models such as T5, GPT-3.5, GPT-4, and LLaMa2 in conjunction with fine-tuning, few-shot, and zero-shot approaches.<n>This research extends its relevance beyond the music industry, proving beneficial in various fields.
• Score: 47.76073133338117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online platforms are increasingly interested in using Data-to-Text technologies to generate content and help their users. Unfortunately, traditional generative methods often fall into repetitive patterns, resulting in monotonous galleries of texts after only a few iterations. In this paper, we investigate LLM-based data-to-text approaches to automatically generate marketing texts that are of sufficient quality and diverse enough for broad adoption. We leverage Language Models such as T5, GPT-3.5, GPT-4, and LLaMa2 in conjunction with fine-tuning, few-shot, and zero-shot approaches to set a baseline for diverse marketing texts. We also introduce a metric JaccDiv to evaluate the diversity of a set of texts. This research extends its relevance beyond the music industry, proving beneficial in various fields where repetitive automated content generation is prevalent.

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