LLMs (Large Language Models) for Misinformation Detection
Abstract People are overwhelmed by a huge amount of news they receive every day. Many times, they may relay the news to their family members or friends if they deem the news is relevant to the family members or friends, but not all the news they receive is true. According to a study, 38% of Americans have the experience of sharing fake news with others, and people usually trust the news more if it is from the persons they know. Various machine learning methods have been used to identify fake news, and each has its pros and cons. Currently, generative AI and LLMs (large language models) are trendy and very popular:

• Generative AI: It is based on deep learning and works by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users’ natural language requests or questions and respond with relevant new content such as text and imagery.
• LLMs (Large Language Models): LLMs are a form of generative AI, and they analyze huge data sets of languages and can comprehend and generate human language text.

Two applications of LLMs related to misinformation management will be considered are

• Using LLMs to identify misinformation and
• Identifying the misinformation generated by LLMs.

LLMs are based on NLP (natural language processing) and misinformation is a kind of text. If the LLMs are well trained, they should be able to tell whether the information is true or false. On the other hand, misinformation could be synthesized by LLMs. How to identify the misinformation synthesized by LLMs could be another research topic.

Keywords
Security, misinformation, misinformation identification, LLMs, large language models, natural language processing

References
Combating misinformation in the age of LLMs: Opportunities and Challenges
Creating a large language model from scratch: A beginner’s guide

Conference