Generation of Explanations for Logic Reasoning
Yanyi Pu
This thesis delves into a fortiori arguments in deductive reasoning,
underscoring their relevance in various domains such as law, philosophy, and
artificial intelligence. The research is centred on employing GPT-3.5-turbo to
automate the analysis of these arguments, with a focus on understanding
intricate reasoning processes, generating clear and coherent explanations, and
creating novel arguments. The methodology encompasses a series of tasks
including detailed reasoning, interpretation, and the augmentation of a
fortiori arguments. It involves meticulously identifying these arguments in
diverse contexts, differentiating comparative elements, and categorizing them
based on their logical structure.
  Extensive experiments reveals the challenges encountered by GPT-3.5-turbo in
accurately detecting and classifying a fortiori arguments. Nevertheless, the
model demonstrates a performance that rivals specialized models, particularly
in extracting key components and interpreting underlying properties. The
integration of external information into the model's processing significantly
elevates the quality of the generated explanations. Additionally, the model
exhibits a noteworthy capability in augmenting arguments, thus contributing to
the enrichment of the data set.
  Despite facing certain limitations, this thesis makes significant
contributions to the fields of artificial intelligence and logical reasoning.
It introduces novel methodologies, establishes a rigorous evaluation framework,
and provides deep insights that set the stage for future advancements in
automated logical reasoning. The findings and methodologies presented herein
not only underscore the potential of AI in complex reasoning tasks but also
highlight areas for future research and development.