EduAdapt：用于评估大语言模型年级适配能力的问答基准数据集
EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs
AI总结：
本文提出覆盖九门理科、1至12年级的近4.8万条问答基准EduAdapt，用于评估开源LLM按学生年级调整表达的能力，并揭示其低年级适配仍存在不足。
摘要：
大语言模型（LLM）正在通过回答问题、解释复杂概念以及生成涵盖众多学科的内容来变革教育。尽管它们在学术基准测试中表现出色，却往往无法根据学生的年级水平定制回答。这在K-12教育中是一项关键需求，因为适合年龄的词汇和解释对于有效学习至关重要。现有模型经常生成对低龄学习者而言过于高深或模糊的输出，而且目前没有标准化基准来评估其在不同认知和发展阶段之间进行调整的能力。为弥补这一空白，我们推出EduAdapt，这是一个包含近4.8万条带有年级标签的问答对的基准，覆盖九个理科学科，跨越1至12年级，并归为四个年级层级。我们在EduAdapt上评估了一系列多样化的开源LLM，发现虽然较大的模型通常表现更好，但它们仍难以为低年级学生（1至5年级）生成合适的回答。我们的工作提出了首个用于评估LLM年级适配能力的数据集和评估框架，旨在通过更好的训练和提示策略，促进发展出更符合发展阶段的教育AI系统。EduAdapt的代码和数据集公开发布于https://github.com/NaumanNaeem/EduAdapt。
英文摘要：
Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong performance on academic benchmarks, they often fail to tailor responses to students' grade levels. This is a critical need in K-12 education, where age-appropriate vocabulary and explanation are essential for effective learning. Existing models frequently produce outputs that are too advanced or vague for younger learners, and there are no standardized benchmarks to evaluate their ability to adjust across cognitive and developmental stages. To address this gap, we introduce EduAdapt, a benchmark of nearly 48k grade-labeled QA pairs across nine science subjects, spanning Grades 1-12 and grouped into four grade levels. We evaluate a diverse set of open-source LLMs on EduAdapt and find that while larger models generally perform better, they still struggle with generating suitable responses for early-grade students (Grades 1-5). Our work presents the first dataset and evaluation framework for assessing grade-level adaptability in LLMs, aiming to foster more developmentally aligned educational AI systems through better training and prompting strategies. EduAdapt code and datasets are publicly available at https://github.com/NaumanNaeem/EduAdapt.
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。
