mlsanityData to 7 Oct 2026

Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning

arXiv 2203.11720 ↗First posted 16 Mar 2022cs.CLcited by 12

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Abstract (arXiv)

Since open social platforms allow for a large and continuous flow of unverified information, rumors can emerge unexpectedly and spread quickly. However, existing rumor detection (RD) models often assume the same training and testing distributions and can not cope with the continuously changing social network environment. This paper proposed a Continual Prompt-Tuning RD (CPT-RD) framework, which avoids catastrophic forgetting (CF) of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. Specifically, we propose the following strategies: (a) Our design explicitly decouples shared and domain-specific knowledge, thus reducing the interference among different domains during optimization; (b) Several technologies aim to transfer knowledge of upstream tasks to deal with emergencies; (c) A task-conditioned prompt-wise hypernetwork (TPHNet) is used to consolidate past domains. In addition, CPT-RD avoids CF without the necessity of a rehearsal buffer.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 28 of its 44 references that are in the map. First posted on arXiv 16 Mar 2022.

Builds on

28 of 44 references.

References · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  4. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  5. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  6. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
  7. 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 863
  8. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 686
  9. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
  10. 2018Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interferencecited by 511
  11. 2019Continual learning with hypernetworkscited by 267
  12. 2019Meta-Learning Representations for Continual Learningcited by 189
16 more
  1. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 152
  2. 2017Detection and Resolution of Rumours in Social Media: A Surveycited by 103
  3. 2021Achieving Forgetting Prevention and Knowledge Transfer in Continual Learningcited by 91
  4. 2020Fighting an Infodemic: COVID-19 Fake News Datasetcited by 82
  5. 2018A Survey on Natural Language Processing for Fake News Detectioncited by 67
  6. 2020La-MAML: Look-ahead Meta Learning for Continual Learningcited by 51
  7. 2020Efficient Meta Lifelong-Learning with Limited Memorycited by 48
  8. 2021Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Taskscited by 46
  9. 2020Meta-Consolidation for Continual Learningcited by 40
  10. 2021Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learningcited by 35
  11. 2022A Simple Hash-Based Early Exiting Approach For Language Understanding and Generationcited by 32
  12. 2019Fake News Early Detection: An Interdisciplinary Studycited by 29
  13. 2021Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networkscited by 29
  14. 2021Multimodal Emergent Fake News Detection via Meta Neural Process Networkscited by 21
  15. 2020AutoRC: Improving BERT Based Relation Classification Models via Architecture Searchcited by 9
  16. 2021Dynamically Addressing Unseen Rumor via Continual Learningcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 5; 2026: 4.

12 papers.

  1. 2024MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuningcited by 19
  2. 2023F-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Taskscited by 18
  3. 2025AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent Systemcited by 15
  4. 2022Multi-modal Misinformation Detection: Approaches, Challenges and Opportunitiescited by 14
  5. 2025PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustmentcited by 9
  6. 2025Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generatorcited by 4
  7. 2026MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicinecited by 4
  8. 2025Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectorscited by 2
  9. 2026Evaluating ChatGPT on Medical Information Extraction Tasks: Performance, Explainability and Beyondcited by 1
  10. 2026ACL: Aligned Contrastive Learning Improves BERT and Multi-exit BERT Fine-tuningcited by 1
  11. 2025Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representationscited by 0
  12. 2026A Generative Model for Joint Multiple Intent Detection and Slot Fillingcited by 0