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.

本论文研究了在演绎推理中的胜于推理论据，强调它们在法律、哲学和人工智能等领域的相关性。研究集中于利用 GPT-3.5-turbo 自动化分析这些论据，并着重于理解复杂的推理过程、生成清晰连贯的解释以及创建新颖的论据。尽管 GPT-3.5-turbo 在准确检测和分类胜于论据方面面临一些挑战，但该模型展现出与专门模型相媲美的性能，尤其是在提取关键组成部分和解释底层属性方面。此外，将外部信息整合到模型的处理过程中显著提高了生成解释的质量。尽管存在某些限制，这篇论文对人工智能和逻辑推理领域做出了重要贡献，引入了新的方法论，建立了严格的评估框架，并提供了深入的见解，为自动化逻辑推理的未来发展奠定了基础。本文所提出的发现和方法不仅凸显了人工智能在复杂推理任务中的潜力，也突出了未来研究和发展的方向。