The spread of fake news has negatively impacted society. Prior efforts in Natural Language Processing (NLP) have employed machine learning models and Pre-trained Language Models (PLMs) like BERT to automate fake news detection with promising results. These models excel at text classification tasks, but their dependence on large context-specific datasets presents a hurdle in the dynamic and ever-evolving context of fake news. The recent emergence of Large Language Models (LLMs) offers a potentially transformative innovation. LLMs have demonstrated great promise in NLP tasks with little additional training data. Compared to PLMs, LLMs have broader knowledge and enhanced reasoning capabilities, suggesting their suitability for fake news detection. This study proposes an investigation which considers multiple perspectives to probe the applicability of the effectiveness, opportunities and challenges associated with leveraging LLMs for automated fake news detection. We leverage multiple state-of-the-art LMMs by using them at multiple levels of guidance to validate their accuracy and task-specific bias. The main contribution of this work is a better understanding of the role LLMs can play in automated fake news detection, the pitfalls that should be avoided when leveraging them, the most effective approaches when employing them, and the future challenges that need to be addressed. Our key contributions include a better understanding of how to employ LLMs for fake news detection, their strengths and weaknesses, and some practical recommendations for deploying them in the real world. Our work shows that while LLMs hold great potential for advancing automated fake news detection, thoughtful consideration of their limitations and careful application refinement are essential for their effective deployment in the fight against fake news.

Researchers at the Delft University of Technology have developed Type4Py: a tool that uses Machine Learning to predict types for Python code. These predictions can be applied by developers to their python code to increase readability and can later be tested by a type-checker for possible type-errors. If a prediction does not return a type-error that prediction is called type-correct. Type4Py has been evaluated by matching its predictions with earlier annotations, also called ground-truth, and has gotten an MRR of 71.7%. However, Type4Py’s predictions have not been evaluated on their typecorrectness. Therefore, I sought out to answer the following research question: How well does Type4Py perform when validated by the static typechecker Mypy? I answered this research question by answering two sub-questions: How many of Type4Py’s predictions are type-correct? And how many of Type4Py’s predictions are type-correct and match ground-truth? I tested a cleaned subset of the ManyTypes4Py dataset with Mypy by running a greedy strategy where I would always pick Type4Py’s prediction with the highest confidence on three different confidence thresholds: 0.25, 0.5 and 0.75 and reached accuracies in terms of typecorrectness of 88%, 91% and 95% for those, respectively. For the case where Type4Py’s predictions matched ground-truth, the predictions on those same thresholds reached accuracies in terms of type-correctness of 95%, 97% and 98%. Comparing this with a similar Type predictor namely, Typilus . Type4Py’s predictions are more typecorrect with a confidence level of at most 50%.