arXiv 2510.139852025-10-15cs.AI

大型语言模型在因果学习中是否表现出偏差？来自偶然性判断的见解

Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgment

María Victoria Carro, Denise Alejandra Mester, Francisca Gauna Selasco, Giovanni Franco Gabriel Marraffini, Mario Alejandro Leiva, Gerardo I. Simari, María Vanina Martinez

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AI总结：

本研究通过医学情境下的1000个零偶然性场景，测试大型语言模型在偶然性判断任务中的表现，发现所有模型均系统性地产生因果幻觉，支持其仅复现因果语言而非真正理解因果关系的假设。

摘要：

因果学习是一种认知过程，即基于可用信息发展出进行因果推断的能力，这一过程通常受规范性原则指导。该过程容易产生错误和偏差，例如因果幻觉，即人们尽管缺乏支持性证据，仍会感知到两个变量之间存在因果关系。这种认知偏差被认为潜藏于许多社会问题之下，包括社会偏见、刻板印象形成、错误信息和迷信思维。在本研究中，我们考察大型语言模型在面对经典认知科学范式——偶然性判断任务时，是否容易产生因果幻觉。为探究此问题，我们构建了一个包含1000个零偶然性场景（其中可用信息不足以在变量间建立因果关系）的医学情境数据集，并提示大型语言模型评估潜在原因的有效性。我们的研究结果显示，所有被评估的模型均系统地推断出无根据的因果关系，揭示出其对因果幻觉的强烈易感性。尽管关于大型语言模型是真正理解因果关系还是仅复现因果语言而无真正理解存在持续争论，我们的发现支持后一种假设，并对在需要准确因果推理以进行明智决策的领域中使用语言模型提出担忧。

英文摘要：

Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process is prone to errors and biases, such as the illusion of causality, in which people perceive a causal relationship between two variables despite lacking supporting evidence. This cognitive bias has been proposed to underlie many societal problems, including social prejudice, stereotype formation, misinformation, and superstitious thinking. In this work, we examine whether large language models are prone to developing causal illusions when faced with a classic cognitive science paradigm: the contingency judgment task. To investigate this, we constructed a dataset of 1,000 null contingency scenarios (in which the available information is not sufficient to establish a causal relationship between variables) within medical contexts and prompted LLMs to evaluate the effectiveness of potential causes. Our findings show that all evaluated models systematically inferred unwarranted causal relationships, revealing a strong susceptibility to the illusion of causality. While there is ongoing debate about whether LLMs genuinely understand causality or merely reproduce causal language without true comprehension, our findings support the latter hypothesis and raise concerns about the use of language models in domains where accurate causal reasoning is essential for informed decision-making.

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