# Cross-Domain Fake News Detection Using a Prompt-Based Approach
###### Abstract
[3] pioneered the Pre-Trained Prompt Tuning (PPT) framework, leveraging prompts to optimize PLMs for downstream tasks. Similarly, [PERSON] et al. [4] showcased the efficacy of prompt-based methods in leveraging GPT-3 for Grammatical Error Correction (GEC) tasks. Further advancements by [PERSON] et al. [5] in clinical named entity recognition (NER) tasks using GPT-3.5 and GPT-4 underscore the versatility of task-specific prompts in augmenting PLMs' capabilities. Additionally, [PERSON] et al. [6] highlighted the efficacy of prompt tuning in enhancing the performance of frozen language models across specific downstream tasks. In [7], the authors introduced Pattern-Exploiting Training (PET), a novel semi-supervised training approach designed to enhance the performance of language models in low-resource settings. In the context of text classification, a crucial technique known as prompt engineering is commonly employed. This approach involves strategically including additional information (i.e., a prompt), denoted as \(P\), before the actual input text \(X\). By conditioning a model on this combined input \([P;X]\), the aim is to maximize the likelihood of the model predicting the correct class label \(Y\). This is achieved while keeping the model's internal parameters \(\theta\) unchanged. In essence, prompt-based approaches reinterpret the task as a blank-filling problem, where a token (representing the label) is predicted using a verbalizer applied to a given prompt with a task-specific template. Inspired by the study findings presented in [2], we present a prompt-based GPT2 model for FND. Our approach involves designing a template that focuses on incorporating contextual information from input sentences to guide the fine-tuning process of the language model. This template, instantiated as a prompt, serves as a cue for the model to discern the credibility of the input text and predict the binary classification probability of whether the text corresponds to the label of interest (e.g., \"fake\" or \"real\"). For instance, with a given sentence \(x\), e.g., \"This news article discusses the latest technology trends\", we construct a prompt template by appending a blank token to the sentence, as follows: \(x\) + prompt \(\xrightarrow{}\). Subsequently, this template is passed through our model, and the generated words in the blank token are mapped into categories via a process we refer to as verbalization. In this work, we demonstrate that guiding PLMs to identify fake news in a zero-shot capacity (i.e., based on zero-shot transfer or zero-shot learning, which usually refers to the task of training a model on one set of data and then evaluating it on a different set of data) can be facilitated by harnessing the knowledge acquired during pre-training. This approach diminishes the necessity for gathering data specifically for task-oriented supervision, thereby alleviating the challenges posed by data scarcity in certain scenarios. We conducted various experiments on a publicly available dataset that encompasses different domains, and the results show that our method can achieve better or on-par performance than the current SOTA methods. In addition, we evaluated our model's performance in zero-shot settings and found that it greatly outperformed SOTA baselines. Our framework can detect, with promising accuracy, fake news across different domains without any prior knowledge or training on fake content derived from certain domains. The task we employed to test our method can be seen as a zero-shot cross-domain FND task.