Acting Flatterers via LLMs Sycophancy: Combating Clickbait with LLMs Opposing-Stance Reasoning

• URL: http://arxiv.org/abs/2601.12019v1
• Date: Sat, 17 Jan 2026 11:57:23 GMT
• Title: Acting Flatterers via LLMs Sycophancy: Combating Clickbait with LLMs Opposing-Stance Reasoning
• Authors: Chaowei Zhang, Xiansheng Luo, Zewei Zhang, Yi Zhu, Jipeng Qiang, Longwei Wang,
• Abstract summary: Sycophancy is a tendency to produce reasoning that matches users' beliefs over truthful ones.<n>This work proposes a novel approach that harnesses this behavior to generate contrastive reasoning from opposing perspectives.<n>We develop a local Opposing Reasoning-based Clickbait Detection model that integrates three BERT encoders to represent the title and its associated reasoning.
• Score: 10.782330552067664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread proliferation of online content has intensified concerns about clickbait, deceptive or exaggerated headlines designed to attract attention. While Large Language Models (LLMs) offer a promising avenue for addressing this issue, their effectiveness is often hindered by Sycophancy, a tendency to produce reasoning that matches users' beliefs over truthful ones, which deviates from instruction-following principles. Rather than treating sycophancy as a flaw to be eliminated, this work proposes a novel approach that initially harnesses this behavior to generate contrastive reasoning from opposing perspectives. Specifically, we design a Self-renewal Opposing-stance Reasoning Generation (SORG) framework that prompts LLMs to produce high-quality agree and disagree reasoning pairs for a given news title without requiring ground-truth labels. To utilize the generated reasoning, we develop a local Opposing Reasoning-based Clickbait Detection (ORCD) model that integrates three BERT encoders to represent the title and its associated reasoning. The model leverages contrastive learning, guided by soft labels derived from LLM-generated credibility scores, to enhance detection robustness. Experimental evaluations on three benchmark datasets demonstrate that our method consistently outperforms LLM prompting, fine-tuned smaller language models, and state-of-the-art clickbait detection baselines.

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