mlsanityData to 7 Oct 2026

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

arXiv 2512.18352 ↗First posted 20 Dec 2025cs.CL cs.AIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

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 textitearly time point determination, while the LLM serves as a powerful textitrumor 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 25 of its 66 references that are in the map. First posted on arXiv 20 Dec 2025.

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25 of 66 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Stance Detection with Collaborative Role-Infused LLM-Based Agents16 Oct

Other references · 24

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Generative Adversarial Imitation Learningcited by 1,576
  3. 2017Constrained Policy Optimizationcited by 928
  4. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  5. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  6. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  7. 2023Sentiment Analysis in the Era of Large Language Models: A Reality Checkcited by 262
  8. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 152
  9. 2021COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemiccited by 100
  10. 2021Towards Few-Shot Fact-Checking via Perplexitycited by 63
  11. 2023Probing LLMs for hate speech detection: strengths and vulnerabilitiescited by 52
  12. 2022Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learningcited by 41
12 more
  1. 2020Constrained Markov Decision Processes via Backward Value Functionscited by 33
  2. 2022Zero-Shot Rumor Detection with Propagation Structure via Prompt Learningcited by 32
  3. 2024JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claimscited by 29
  4. 2018CED: Credible Early Detection of Social Media Rumorscited by 26
  5. 2024An Investigation of Large Language Models for Real-World Hate Speech Detectioncited by 20
  6. 2020No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detectioncited by 13
  7. 2024Can Large Language Models Detect Rumors on Social Media?cited by 9
  8. 2023Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Datasetcited by 7
  9. 2023Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Modelscited by 6
  10. 2024Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Modelscited by 5
  11. 2017On Early-stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learnerscited by 2
  12. 2024Imitating Cost-Constrained Behaviors in Reinforcement Learningcited by 1

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  6. 2025Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator
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