COOL: Comprehensive Knowledge Enhanced Prompt Learning for Domain Adaptive Few-shot Fake News Detection

• URL: http://arxiv.org/abs/2406.10870v1
• Date: Sun, 16 Jun 2024 09:41:25 GMT
• Title: COOL: Comprehensive Knowledge Enhanced Prompt Learning for Domain Adaptive Few-shot Fake News Detection
• Authors: Yi Ouyang, Peng Wu, Li Pan,
• Abstract summary: We propose COOL, a comprehensive knedge enhanced prOmpt Learning method for domain adaptive few-shot FND.Owl. Specifically, we propose a comprehensive knowledge extraction module to extract both structured and unstructured knowledge that are positively or negatively correlated with news from external sources.
• Score: 16.478355864072814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most Fake News Detection (FND) methods often struggle with data scarcity for emerging news domain. Recently, prompt learning based on Pre-trained Language Models (PLM) has emerged as a promising approach in domain adaptive few-shot learning, since it greatly reduces the need for labeled data by bridging the gap between pre-training and downstream task. Furthermore, external knowledge is also helpful in verifying emerging news, as emerging news often involves timely knowledge that may not be contained in the PLM's outdated prior knowledge. To this end, we propose COOL, a Comprehensive knOwledge enhanced prOmpt Learning method for domain adaptive few-shot FND. Specifically, we propose a comprehensive knowledge extraction module to extract both structured and unstructured knowledge that are positively or negatively correlated with news from external sources, and adopt an adversarial contrastive enhanced hybrid prompt learning strategy to model the domain-invariant news-knowledge interaction pattern for FND. Experimental results demonstrate the superiority of COOL over various state-of-the-arts.

Related papers

• Preserving Generalization of Language models in Few-shot Continual Relation Extraction [34.68364639170838]
  Few-shot Continual Relations Extraction (FCRE) is an emerging and dynamic area of study. We introduce a novel method that leverages often-discarded language model heads. Our experimental results underscore the efficacy of the proposed method and offer valuable insights for future work.
  arXiv  Detail & Related papers  (2024-10-01T02:22:34Z)
• Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection [47.01850264003063]
  Few-Shot Fake News Detection aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. We propose a Dual-perspective Augmented Fake News Detection model, designed to enhance Large Language Models.
  arXiv  Detail & Related papers  (2024-07-12T03:15:01Z)
• Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning [13.371405067535814]
  This paper investigates the effectiveness ofSupervised Fine-Tuning (SFT) as a method for knowledge injection in Large Language Models (LLMs) We compare different dataset generation strategies -- token-based and fact-based scaling -- to create training data that helps the model learn new information. Our results show considerable performance improvements in Q&A tasks related to out-of-domain knowledge.
  arXiv  Detail & Related papers  (2024-03-30T01:56:07Z)
• Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models [53.52344131257681]
  We propose a new paradigm for fine-tuning called F-Learning, which employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge. Experimental results on two publicly available datasets demonstrate that our proposed F-Learning can obviously improve the knowledge updating performance of both full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2023-11-14T09:12:40Z)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer [65.42096702428347]
  Graph Neural Networks (GNNs) aggregate information from neighboring nodes. Knowledge Bridge Learning (KBL) learns a knowledge-enhanced posterior distribution for target domains. Bridged-GNN includes an Adaptive Knowledge Retrieval module to build Bridged-Graph and a Graph Knowledge Transfer module.
  arXiv  Detail & Related papers  (2023-08-18T12:14:51Z)
• Plug-and-Play Knowledge Injection for Pre-trained Language Models [116.37916535076478]
  Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. Massive retraining is required to deploy new knowledge injection methods or knowledge bases for downstream tasks. We study how to improve the flexibility and efficiency of knowledge injection by reusing existing downstream models.
  arXiv  Detail & Related papers  (2023-05-28T10:58:00Z)
• UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models [100.4659557650775]
  We propose a UNified knowledge inTERface, UNTER, to provide a unified perspective to exploit both structured knowledge and unstructured knowledge. With both forms of knowledge injected, UNTER gains continuous improvements on a series of knowledge-driven NLP tasks.
  arXiv  Detail & Related papers  (2023-05-02T17:33:28Z)
• Domain Adaptive Fake News Detection via Reinforcement Learning [34.95213747705498]
  We introduce a novel reinforcement learning-based model called REAL-FND to detect fake news. Experiments on real-world datasets illustrate the effectiveness of the proposed model.
  arXiv  Detail & Related papers  (2022-02-16T16:05:37Z)
• Ontology-enhanced Prompt-tuning for Few-shot Learning [41.51144427728086]
  Few-shot Learning is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to benefit the few-shot setting in various tasks.
  arXiv  Detail & Related papers  (2022-01-27T05:41:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.