Efficient argument classification with compact language models and ChatGPT-4 refinements

Argument mining (AM) is a multidisciplinary research field encompassing diverse areas such as logic and philosophy, language, rhetoric and law, psychology, and computer science. The theory of argumentation and the use of logical reasoning to justify claims and conclusions is an extensively studied field, but the application of data science methods to automate these processes is a relatively recent development. In nearly every field, the ability to automatically extract arguments and their relationships from the input source is of significant importance. Over the last decade, AM has become one of the core studies within artificial intelligence [1, 2] due to its ability to conjugate representational needs with user-related cognitive models and computational models for automated reasoning [3]. As a subfield of Natural Language Processing (NLP) and computational linguistics, AM focuses on automatically identifying, extracting, and analyzing argumentative structures within natural language texts, which includes recognizing core components of arguments, such as claims and evidence [4].