研究对话式AI中隐蔽广告的生成与检测，提升内容透明度。
JU-NLP at Touché: Covert Advertisement in Conversational AI-Generation and Detection Strategies
- 基于用户上下文生成隐性广告，通过提示工程和数据微调增强隐蔽性。
- 广告生成精度达1.0，召回率0.71；检测模型F1值高达0.99~1.00。
- 适合关注AI伦理、内容安全与广告合规的研究者与开发者。
本文提出一种针对对话式AI系统中隐蔽广告的完整框架，涵盖生成与检测两方面。在生成方面（子任务1），提出新框架，利用用户上下文和查询意图生成情境相关广告，结合先进提示策略与配对训练数据，微调大语言模型（LLM）以提升隐蔽性。在检测方面（子任务2），探索两种有效策略：使用微调的CrossEncoder（all-mpnet-base-v2）进行直接分类，以及基于提示重构的微调DeBERTa-v3-base模型。两者均仅依赖响应文本，具备实际部署可行性。实验表明，生成任务实现精度1.0、召回率0.71，检测任务F1得分在0.99至1.00之间。结果证实了该方法在说服性传播与透明度之间的平衡潜力。
原文摘要 · Abstract (English)
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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