JU-NLP at Touché: Covert Advertisement in Conversational AI-Generation and Detection Strategies

• URL: http://arxiv.org/abs/2509.14256v1
• Date: Fri, 12 Sep 2025 14:53:56 GMT
• Title: JU-NLP at Touché: Covert Advertisement in Conversational AI-Generation and Detection Strategies
• Authors: Arka Dutta, Agrik Majumdar, Sombrata Biswas, Dipankar Das, Sivaji Bandyopadhyay,
• Abstract summary: It explores how subtle promotional content can be crafted within AI-generated responses and introduces methods to identify and mitigate such covert advertising strategies.<n>For generation (Sub-Task1), we propose a novel framework that leverages user context and query intent to produce contextually relevant advertisements.<n>For detection (Sub-Task2), we explore two effective strategies: a fine-tuned CrossEncoder (textttall-mpnet-base-v2) for direct classification, and a prompt-based reformulation using a fine-tuned textttDeBERTa-v3-base
• Score: 22.324944278737167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a comprehensive framework for the generation of covert advertisements within Conversational AI systems, along with robust techniques for their detection. It explores how subtle promotional content can be crafted within AI-generated responses and introduces methods to identify and mitigate such covert advertising strategies. For generation (Sub-Task~1), we propose a novel framework that leverages user context and query intent to produce contextually relevant advertisements. We employ advanced prompting strategies and curate paired training data to fine-tune a large language model (LLM) for enhanced stealthiness. For detection (Sub-Task~2), we explore two effective strategies: a fine-tuned CrossEncoder (\texttt{all-mpnet-base-v2}) for direct classification, and a prompt-based reformulation using a fine-tuned \texttt{DeBERTa-v3-base} model. Both approaches rely solely on the response text, ensuring practicality for real-world deployment. Experimental results show high effectiveness in both tasks, achieving a precision of 1.0 and recall of 0.71 for ad generation, and F1-scores ranging from 0.99 to 1.00 for ad detection. These results underscore the potential of our methods to balance persuasive communication with transparency in conversational AI.

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