Assessing Large Language Models for information verification in Portuguese

In an increasingly interconnected world driven by digital technologies, the ability to navigate, critically evaluate, and effectively use information is more essential than ever. However, amidst the vast amount of online information, misinformation proliferates, posing significant challenges to informed decision-making. With the advent of Large Language Models (LLMs), there is a potential solution in using these models to verify the accuracy of circulating information online. Different Artificial Intelligence models offer distinct capabilities in processing and understanding large amounts of textual data. This research aims to explore and compare the effectiveness of these models in the context of information verification. The objectives of this thesis are: to expand the knowledge of the state of the art in the area of information verification using Artificial Intelligence; to identify and select appropriate methods for developing a solution that aims to investigate the ability of Large Language Models to verify the truthfulness of a text, compared to a knowledge base; to use open-source models (LLaMA, Vicuna, Mistral, etc.) and proprietary ones (GPT-4, BARD, Claude, etc.) to verify information; to analyze the results obtained in experiments, comparing the performance of different LLMs with the existing state of the art and to draw pertinent conclusions based on these results. The student should have significant programming experience, and practical knowledge of machine learning languages and environments, such as PyTorch or TensorFlow. Notes: The work will be be developed in cooperation with SIED (Serviço de Informações Estratégicas de Defesa), which have significant expertise in the the topic. The selected student will have access to the facilities of INESC-ID and the MLKD group ( https://mlkd.idss.inesc-id.pt/ ), including computing facilities that include four DELL PowerEdge C41402 servers, eight NVIDIA 32GB Tesla V100, four NVIDIA 48GB A40 and four NVIDIA 64GB Tesla A100, among other computing servers ( https://mlkd.idss.inesc-id.pt/cluster )