Students’ wrong answers in science assessments can reveal plausible misconceptions, yet standard LLM benchmarks usually evaluate only answer correctness. We introduce MisconceptionBench, a benchmark for evaluating whether LLMs can reason about misconception-linked distractors and empirical student response patterns. It contains 761 K-12 science items, 1,474 expert-annotated distractors, and response distributions from more than 150,000 students. We evaluate eight LLMs on three tasks: misconception matching, misconception generation, and student response distribution prediction. In matching, text-similarity baselines remain competitive in two- and three-candidate settings; clear LLM gains emerge only with four candidates, where GPT-4o and Llama-3.3-70B significantly outperform TF-IDF and MiniLM. In generation, the strongest models are tightly clustered, with Hit@1 of 38.5-39.6%, while retrieval-only baselines recover many targets without an LLM. Response distribution prediction remains difficult: no model exceeds F1=0.21 for identifying grade-band differences. Overall, misconception-centered reasoning requires evaluation beyond answer accuracy.

“古籍文本中的文字通假现象较为常见,这不仅为人理解文意造成了困难,也是古汉语信息处理面临的一项重要挑战。为了服务于通假字的人工判别和机器处理,本文构建并开源了一个多维度的通假字资源库,包括语料库、知识库和评测数据集三个子库。其中,语料库收录11000余条包含通假现象详细标注的语料;知识库以汉字为节点,通假和形声关系为边,从字音、字形、字义多个角度对通假字与正字的属性进行加工,共包含4185个字节点和8350对关联信息;评测数据集面向古汉语信息处理需求,支持通假字检测和正字识别两个子任务的评测,收录评测数据19678条。在此基础上,本文搭建了通假字自动识别的系列基线模型,并结合试验结果分析了影响通假字自动识别的因素与改进方法。进一步地,本文探讨了该资源库在古籍整理、人文研究和文言文教学中的应用。”