大語(yǔ)言模型驅(qū)動(dòng)的多模態(tài)實(shí)驗(yàn)報(bào)告自動(dòng)批改

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中圖分類號(hào)：TP391.4；TP181 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：2096-4706（2025）12-0079-06

Automatic Correction of Multimodal Experimental Reports Driven by LLM

XU Jining， HUANG Nan， SONG Hao （SchoolofElectricalandControlEngineering，North China UniversityofTechnology，Beijing10o144，China）

Abstract：Automatic correction of experimental reports isan important task in the field of intellgent education. FollowingteOBEconcept，tepaperreproduces thteacher'sorrctionideatransfoms tesoingitesitoquestons，and coordinates temultimodalresposeiformatiosuchastext，tables，ndpictures tosoresostoblosetoteactuateaching and curiculum construction needs.Intheunderstanding and scoring stageof multimodal information，on the basisof Deep Learming，LLMisitroduced torealizethecontentextractionandtransformationoftablequestions，andsolvethdifculties of positioningandlogicaldiscrimination.Fortextcontent，BERTisusedtounderstand.Fortheimagecontent，theself-training modelconstructed by thecombination ofBERTand ResNet-18 isused to scale the image matching weightsforthe image featureevaluationingraphicquestions.Theschemeusessmallsampledata fortraining，adapts todiferent subject experiments， andovercomes the pain points such as insuffcient generalization and migration caused byrelying onalarge amount of data training.Through the correction test of two courses，the average accuracy of the report score reaches 92.20% ，bridging the gap of automatic correction of non-customized experimental reports.

Keywords： automatic correction of experimental report; Deep Learning; LLM

0 引言

隨著人工智能技術(shù)融入教育，各類作業(yè)考試的自動(dòng)評(píng)分系統(tǒng)使教師的工作變得更加高效。（剩余7185字）