Claim identification and verification by leveraging prompts and deep learning techniques

Abstract

The intersection of prompt engineering and deep learning represents a promising frontier in the field of identification and verification, particularly in the context of claim verification. This research focuses on creating a comprehensive claim verification dataset using advanced prompt engineering techniques. The goal is to harness the power of prompt engineering to enhance the initial stages of data preparation, which is crucial for training effective deep learning models. By leveraging prompt engineering, the research aims to streamline the process of claim generation and annotation, reducing the time and effort required while improving the quality and diversity of the dataset. Subsequently, deep learning models are employed to classify claims and identify supporting or refuting text spans. Evaluating these models against the prompt-engineered dataset, the study demonstrates how prompt engineering improves model accuracy, efficiency, and interpretability. This integrated approach offers a robust solution to misinformation, ensuring the dissemination of reliable information and showcasing the synergistic potential of prompt engineering and deep learning in claim verification.