This paper introduces MalAlgoQA, a novel dataset designed to evaluate the
counterfactual reasoning capabilities of Large Language Models (LLMs) through a
pedagogical approach. The dataset comprises mathematics and reading
comprehension questions, each accompanied by four answer choices and their
corresponding rationales. We focus on the incorrect answer rationales, termed
"malgorithms", which highlights flawed reasoning steps leading to incorrect
answers and offers valuable insights into erroneous thought processes. We also
propose the Malgorithm Identification task, where LLMs are assessed based on
their ability to identify corresponding malgorithm given an incorrect answer
choice. To evaluate the model performance, we introduce two metrics: Algorithm
Identification Accuracy (AIA) for correct answer rationale identification, and
Malgorithm Identification Accuracy (MIA) for incorrect answer rationale
identification. The task is challenging since state-of-the-art LLMs exhibit
significant drops in MIA as compared to AIA. Moreover, we find that the
chain-of-thought prompting technique not only fails to consistently enhance
MIA, but can also lead to underperformance compared to simple prompting. These
findings hold significant implications for the development of more
cognitively-inspired LLMs to improve their counterfactual reasoning abilities,
particularly through a pedagogical perspective where understanding and
rectifying student misconceptions are crucial.

该论文介绍了 MalAlgoQA，这是一个用于评估大型语言模型（LLMs）通过教育方法的反事实推理能力的新型数据集。该数据集包含数学和阅读理解问题，每个问题都附有四个答案选项及其对应的解释。我们关注不正确答案解释，称为 “malgorithms”，强调导致错误答案的错误推理步骤，并提供有关错误思维过程的有价值的见解。我们还提出了 Malgorithm 识别任务，用于根据给定的错误答案选择来评估 LLMs 识别相应 malgorithm 的能力。为了评估模型性能，我们引入了两个指标：正确答案解释的算法识别准确率（AIA）和错误答案解释的 malgorithm 识别准确率（MIA）。该任务具有挑战性，因为最先进的 LLMs 在 MIA 方面表现出较大的下降。此外，我们发现，“思维链提示” 技术不仅无法始终提高 MIA，而且与简单提示相比也可能导致性能不佳。这些发现在更注重教育视角的更具认知启发的 LLMs 的开发中具有重要意义，以提高其反事实推理能力，特别是在理解和纠正学生错误观念方面。