Research in AI using Large-Language Models (LLMs) is rapidly evolving, and the comparison of their performance with human reasoning has become a key concern. Prior studies have indicated that LLMs and humans share similar biases, such as dismissing logically valid inferences that contradict common beliefs. However, criticizing LLMs for these biases might be unfair, considering our reasoning not only involves formal deduction but also abduction, which draws tentative conclusions from limited information. Abduction can be regarded as the inverse form of syllogism in its basic structure, that is, a process of drawing a minor premise from a major premise and conclusion. This paper explores the accuracy of LLMs in abductive reasoning by converting a syllogistic dataset into one suitable for abduction. It aims to investigate whether the state-of-the-art LLMs exhibit biases in abduction and to identify potential areas for improvement, emphasizing the importance of contextualized reasoning beyond formal deduction. This investigation is vital for advancing the understanding and application of LLMs in complex reasoning tasks, offering insights into bridging the gap between machine and human cognition.

翻译：使用大语言模型（LLMs）的人工智能研究正在迅速发展，其性能与人类推理能力的比较已成为一个关键议题。先前研究表明，LLMs与人类存在相似的认知偏差，例如会否定那些与普遍信念相悖但在逻辑上有效的推论。然而，鉴于我们的推理不仅涉及形式演绎，还包括从有限信息中得出试探性结论的溯因推理，仅因这些偏差而批评LLMs可能并不公允。从基本结构上看，溯因可视为三段论的逆形式，即从大前提和结论推导出小前提的过程。本文通过将三段论数据集转化为适用于溯因推理的形式，探究LLMs在溯因推理中的准确性。研究旨在考察最先进的LLMs是否在溯因推理中存在偏差，并识别潜在的改进方向，强调超越形式演绎的语境化推理的重要性。这项研究对于深化对LLMs在复杂推理任务中表现的理解与应用至关重要，为弥合机器认知与人类认知之间的差距提供了新的见解。