MalAlgoQA：大语言模型反事实推理的教学法评估及其对教育人工智能的启示
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education
AI总结：
本文提出用于评估大语言模型反事实推理能力的教学法数据集MalAlgoQA，设计错误算法识别任务及对应指标，发现顶尖大模型在错误推理识别上表现显著下滑，且思维链提示无稳定提升效果，为教育AI系统开发提供参考。
摘要：
本文提出了MalAlgoQA这一全新数据集，旨在通过教学法路径评估大语言模型（LLM）的反事实推理能力。该数据集包含数学题与阅读理解题，每道题配有四个选项及对应的推理依据。MalAlgoQA的核心是“malgorithms（错误算法推理）”——即错误选项背后的推理逻辑，代表了存在缺陷但逻辑自洽的推理路径。这些错误算法推理可作为反事实场景，用于评估LLM识别与分析缺陷推理模式的能力。\n我们提出了“错误算法识别”任务，即给定错误选项时，评估LLM识别对应错误算法推理的能力。为评估模型表现，我们引入两项指标：用于评估正确选项推理识别能力的算法识别准确率（AIA），以及用于评估错误选项推理识别能力的错误算法识别准确率（MIA）。实验结果显示，当前最先进的LLM在MIA上的表现较AIA出现显著下滑，凸显了反事实推理的难度。出人意料的是，我们发现思维链提示技术不仅无法持续提升MIA，有时甚至比简单提示的表现更差。这些发现对开发反事实推理能力更强的LLM具有重要启示，尤其适用于AI驱动的辅导系统——这类系统中识别并纠正学生的错误认知至关重要。MalAlgoQA数据集可通过以下链接获取：https://github.com/luffycodes/MalAlgoQA-Dataset。
英文摘要：
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. At the heart of MalAlgoQA are ``malgorithms'' - rationales behind incorrect answer choices that represent flawed yet logically coherent reasoning paths. These malgorithms serve as counterfactual scenarios, allowing us to assess an LLM's ability to identify and analyze flawed reasoning patterns. We 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. Our experiments reveal that state-of-the-art LLMs exhibit significant performance drops in MIA compared to AIA, highlighting the challenges in counterfactual reasoning. Surprisingly, we find that the chain-of-thought prompting technique not only fails to consistently enhance MIA but can sometimes lead to underperformance compared to simple prompting. These findings have important implications for developing LLMs with improved counterfactual reasoning, particularly relevant for AI-powered tutoring systems, where identifying and addressing student misconceptions is essential. MalAlgoQA dataset is available \href{https://github.com/luffycodes/MalAlgoQA-Dataset}{here}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
