LLM-based Few-Shot Early Rumor Detection with Imitation Agent
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910004772077568 |
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| author | Zeng, Fengzhu Shao, Qian Cheng, Ling Gao, Wei Cheng, Shih-Fen Ma, Jing Niu, Cheng |
| author_facet | Zeng, Fengzhu Shao, Qian Cheng, Ling Gao, Wei Cheng, Shih-Fen Ma, Jing Niu, Cheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-based Few-Shot Early Rumor Detection with Imitation Agent Zeng, Fengzhu Shao, Qian Cheng, Ling Gao, Wei Cheng, Shih-Fen Ma, Jing Niu, Cheng Computation and Language Artificial Intelligence 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. |
| title | LLM-based Few-Shot Early Rumor Detection with Imitation Agent |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2512.18352 |