6 papers · 1 filter
From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG
Yucheng Chu, Haoyu Han, Shen Dong +6
Automated short answer grading (ASAG) is critical for scaling educational assessment, yet large language models (LLMs) often struggle with hallucinations and strict rubric adherenc…
LLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning
Yu Zhu, Kai Yang
The reasoning capability of large language models (LLMs), defined as their ability to analyze, infer, and make decisions based on input information, is essential for building intel…
Iterative LLM-Based Generation and Refinement of Distracting Conditions in Math Word Problems
Kaiqi Yang, Hang Li, Yucheng Chu +3
Mathematical reasoning serves as a crucial testbed for the intelligence of large language models (LLMs), and math word problems (MWPs) are a popular type of math problems. Most MWP…
Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation
Yucheng Chu, Peng He, Hang Li +6
Short answer assessment is a vital component of science education, allowing evaluation of students' complex three-dimensional understanding. Large language models (LLMs) that posse…
LLM-based Automated Grading with Human-in-the-Loop
Yucheng Chu, Hang Li, Kaiqi Yang +2
The rise of artificial intelligence (AI) technologies, particularly large language models (LLMs), has brought significant advancements to the field of education. Among various appl…
Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions
Hang Li, Tianlong Xu, Kaiqi Yang +5
The rise of large language models (LLMs) offers new opportunities for automatic error detection in education, particularly for math word problems (MWPs). While prior studies demons…