Automated Assessment of Comprehension Strategies from Self-Explanations Using LLMs

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-10-14 DOI:10.3390/info14100567
Bogdan Nicula, Mihai Dascalu, Tracy Arner, Renu Balyan, Danielle S. McNamara
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Abstract

Text comprehension is an essential skill in today’s information-rich world, and self-explanation practice helps students improve their understanding of complex texts. This study was centered on leveraging open-source Large Language Models (LLMs), specifically FLAN-T5, to automatically assess the comprehension strategies employed by readers while understanding Science, Technology, Engineering, and Mathematics (STEM) texts. The experiments relied on a corpus of three datasets (N = 11,833) with self-explanations annotated on 4 dimensions: 3 comprehension strategies (i.e., bridging, elaboration, and paraphrasing) and overall quality. Besides FLAN-T5, we also considered GPT3.5-turbo to establish a stronger baseline. Our experiments indicated that the performance improved with fine-tuning, having a larger LLM model, and providing examples via the prompt. Our best model considered a pretrained FLAN-T5 XXL model and obtained a weighted F1-score of 0.721, surpassing the 0.699 F1-score previously obtained using smaller models (i.e., RoBERTa).
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基于llm的自我解释理解策略的自动评估
在当今信息丰富的世界中,文本理解是一项必不可少的技能,自我解释练习可以帮助学生提高对复杂文本的理解。本研究集中于利用开源大型语言模型(llm),特别是FLAN-T5,来自动评估读者在理解科学、技术、工程和数学(STEM)文本时采用的理解策略。实验依赖于三个数据集(N = 11,833)的语料库,这些数据集在4个维度上标注了自我解释:3种理解策略(即桥接、阐述和释义)和整体质量。除了FLAN-T5,我们还考虑了gpt3.5 turbo,以建立更强的基线。我们的实验表明,通过微调,拥有更大的LLM模型,并通过提示符提供示例,性能得到了提高。我们的最佳模型考虑了预训练的FLAN-T5 XXL模型,并获得了0.721的加权f1分数,超过了之前使用较小模型(即RoBERTa)获得的0.699 f1分数。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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