MATHWELL：利用教师标注生成教育性数学应用题
MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations
AI总结：
为解决K-8数学应用题编写耗时且教育适宜性难量化的问题，研究利用教师专家标注微调70B语言模型，开发出首个针对教育适宜性的应用题生成器MATHWELL，其生成质量匹敌GPT-4且更适宜学生阅读。
摘要：
数学应用题是K-8教育的重要工具，但编写它们耗时且需要大量专业知识。为了具备教育价值，题目必须可解、答案准确，最重要的是符合教育适宜性。我们提出，语言模型有潜力通过自动生成应用题来支持K-8数学教育。然而，评估教育适宜性很难量化。我们通过让教师评估LLM生成的题目来填补这一空白，他们发现现有模型和数据往往不符合教育适宜性。随后我们探索自动生成教育性应用题，最终利用我们的专家标注微调了一个70B语言模型。我们的模型MATHWELL是首个针对教育适宜性设计的K-8应用题生成器。进一步的专家研究发现，MATHWELL生成的题目在可解性、准确性和适宜性方面远超公开模型。MATHWELL还匹敌GPT-4的题目质量，同时为K-8学生提供更适宜的阅读水平，并避免生成有害问题。
英文摘要：
Math word problems are critical K-8 educational tools, but writing them is time consuming and requires extensive expertise. To be educational, problems must be solvable, have accurate answers, and, most importantly, be educationally appropriate. We propose that language models have potential to support K-8 math education by automatically generating word problems. However, evaluating educational appropriateness is hard to quantify. We fill this gap by having teachers evaluate problems generated by LLMs, who find existing models and data often fail to be educationally appropriate. We then explore automatically generating educational word problems, ultimately using our expert annotations to finetune a 70B language model. Our model, MATHWELL, is the first K-8 word problem generator targeted at educational appropriateness. Further expert studies find MATHWELL generates problems far more solvable, accurate, and appropriate than public models. MATHWELL also matches GPT-4's problem quality while attaining more appropriate reading levels for K-8 students and avoiding generating harmful questions.
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。
