SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

Key points

• In this work, we focus on low resource fake news detection, where only a small labeled set is available for training, while evaluation is performed under a fixed and unchanged test protocol.1
• Existing data augmentation methods, such as synonym substitution, generative adversarial networks, and large language model (LLM)-based generation, often produce fluent but overly generic content, lacking the task-specific relevance needed for accurate detection.1
• To address this, we propose SharP, a boundary-aware text generation framework guided by soft and hard prompts, which leverages LLMs as tools adapted to produce task-specific synthetic samples.1
• SharP employs a dual prompt mechanism: hard prompts provide structured guidance and factual constraints, while soft prompts introduce learned semantic patterns from the data.1

Sources (1)

• [1]SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:25 AM

  Fake news detection under low resource conditions is challenged by the scarcity of labeled data and the difficulty of capturing subtle deceptive patterns.

  In this work, we focus on low resource fake news detection, where only a small labeled set is available for training, while evaluation is performed under a fixed and unchanged test protocol.

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