测试大模型在类三段论逆向推理中的表现，发现其推理偏差与人类相似。
Abductive Reasoning with Syllogistic Forms in Large Language Models
- 将三段论数据集转换为逆向推理形式，用于评估大模型的溯因能力。
- 模型在违背常识的推理中准确率下降，表现出与人类类似的认知偏见。
- 强调情境化推理对超越形式逻辑的重要性，适合研究认知偏差的学者。
大型语言模型（LLMs）在人工智能领域的研究快速发展，其与人类推理能力的对比成为关注焦点。已有研究表明，LLMs 和人类均存在类似偏见，例如忽略与常识相悖但逻辑有效的推理。然而，这种批评可能不公，因为人类推理不仅包含形式演绎，还涉及溯因——从有限信息中做出暂定结论。溯因在基本结构上是三段论的逆过程，即从大前提和结论反推小前提。本文通过将三段论数据集转化为适合溯因的任务，探究当前最先进大模型在溯因推理中的准确性，旨在检验其是否存在溯因偏见，并识别改进方向，强调情境化推理超越形式逻辑的重要性。该研究对推动大模型在复杂推理任务中的理解与应用具有关键意义，有助于弥合机器与人类认知之间的差距。
原文摘要 · Abstract (English)
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.
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