LLM-based Few-Shot Early Rumor Detection with Imitation Agent

• URL: http://arxiv.org/abs/2512.18352v1
• Date: Sat, 20 Dec 2025 12:42:27 GMT
• Title: LLM-based Few-Shot Early Rumor Detection with Imitation Agent
• Authors: Fengzhu Zeng, Qian Shao, Ling Cheng, Wei Gao, Shih-Fen Cheng, Jing Ma, Cheng Niu,
• Abstract summary: Early Rumor Detection (EARD) aims to identify the earliest point at which a claim can be accurately classified based on a sequence of social media posts.<n>Large Language Models (LLMs) perform well in few-shot NLP tasks, but are not well-suited for time-series data.<n>We propose a novel EARD framework that combines an autonomous agent and an LLM-based detection model.
• Score: 16.230257899856046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early Rumor Detection (EARD) aims to identify the earliest point at which a claim can be accurately classified based on a sequence of social media posts. This is especially challenging in data-scarce settings. While Large Language Models (LLMs) perform well in few-shot NLP tasks, they are not well-suited for time-series data and are computationally expensive for both training and inference. In this work, we propose a novel EARD framework that combines an autonomous agent and an LLM-based detection model, where the agent acts as a reliable decision-maker for \textit{early time point determination}, while the LLM serves as a powerful \textit{rumor detector}. This approach offers the first solution for few-shot EARD, necessitating only the training of a lightweight agent and allowing the LLM to remain training-free. Extensive experiments on four real-world datasets show our approach boosts performance across LLMs and surpasses existing EARD methods in accuracy and earliness.

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