TeamCMU at Touch\'e: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search
To Eun Kim, Jo\~ao Coelho, Gbemileke Onilude, Jai Singh

TL;DR
This paper presents a modular framework for integrating and detecting advertisements in conversational search systems powered by LLMs, using synthetic data and adversarial co-evolution to improve ad stealth and detection robustness.
Contribution
It introduces a novel adversarial co-evolution framework with a modular pipeline, synthetic data training, and classifier-guided ad integration strategies for conversational search.
Findings
High-performing ad classifiers trained on synthetic data.
Classifier-guided ad integration improves ad stealth.
Adversarial co-evolution enhances detection robustness.
Abstract
As conversational search engines increasingly adopt generation-based paradigms powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), the integration of advertisements into generated responses presents both commercial opportunities and challenges for user experience. Unlike traditional search, where advertisements are clearly delineated, generative systems blur the boundary between informational content and promotional material, raising concerns around transparency and trust. In this work, we propose a modular pipeline for advertisement management in RAG-based conversational systems, consisting of an ad-rewriter for seamless ad integration and a robust ad-classifier for detection. We leverage synthetic data to train high-performing classifiers, which are then used to guide two complementary ad-integration strategies: supervised fine-tuning of the ad-rewriter…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · AI in Service Interactions · Digital Marketing and Social Media
