A Multi-Stage Workflow for the Review of Marketing Content with Reasoning Large Language Models

• URL: http://arxiv.org/abs/2601.06054v1
• Date: Fri, 19 Dec 2025 19:40:16 GMT
• Title: A Multi-Stage Workflow for the Review of Marketing Content with Reasoning Large Language Models
• Authors: Alberto Purpura, Emily Chen, Swapnil Shinde,
• Abstract summary: Reasoning Large Language Models (LLMs) have shown promising results when tasked with solving complex problems.<n>We propose and evaluate a multi-stage workflow that leverages the capabilities of fine-tuned reasoning LLMs to assist in the review process of marketing content.
• Score: 1.705490308161302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning Large Language Models (LLMs) have shown promising results when tasked with solving complex problems. In this paper, we propose and evaluate a multi-stage workflow that leverages the capabilities of fine-tuned reasoning LLMs to assist in the review process of marketing content, making sure they comply with a given list of requirements. The contributions of this paper are the following: (i) we present a novel approach -- that does not rely on any external knowledge representation -- for the automatic identification of compliance issues in textual content; (ii) compare the effectiveness of different fine-tuning strategies like Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) in training models to solve this problem; (iii) we evaluate the effectiveness of training small LLMs to generate reasoning tokens before providing their final response; (iv) we evaluate how the choice and combinations of different reward functions affects the performance of a model trained with GRPO.

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