用智能代理+大模型实现少样本谣言早期识别，效果更优且省算力。
LLM-based Few-Shot Early Rumor Detection with Imitation Agent
- 设计智能代理决定最早判断时间点，大模型专注谣言识别。
- 在四个真实数据集上超越现有方法，准确率与发现速度双提升。
- 仅需训练轻量代理，大模型无需微调，适合资源受限场景。
早期谣言检测（EARD）旨在根据社交媒体动态序列，识别出可准确判定某说法为谣言的最早时刻。这在数据稀缺情况下尤为困难。尽管大语言模型（LLMs）在少样本自然语言任务中表现优异，但其对时序数据适应性差，且训练与推理成本高。本文提出一种新型EARD框架，结合自主代理与基于LLM的检测模型：代理负责可靠地确定早期判断时间点，而LLM则作为强大的谣言检测器。该方法首次实现少样本下的EARD，只需训练轻量级代理，使LLM保持无需训练状态。在四个真实世界数据集上的大量实验表明，该方法显著提升各类LLM的性能，并在准确率和发现及时性上优于现有EARD方法。
原文摘要 · Abstract (English)
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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