1 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
Optimizing In-Context Demonstrations for LLM-based Automated Grading
Yucheng Chu, Hang Li, Kaiqi Yang +4
Automated assessment of open-ended student responses is a critical capability for scaling personalized feedback in education. While large language models (LLMs) have shown promise…
Confusion-Aware Rubric Optimization for LLM-based Automated Grading
Yucheng Chu, Hang Li, Kaiqi Yang +4
Accurate and unambiguous guidelines are critical for large language model (LLM) based graders, yet manually crafting these prompts is often sub-optimal as LLMs can misinterpret exp…
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…
Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
Hang Li, Kaiqi Yang, Yucheng Chu +2
Large language models (LLMs) have been widely used for problem-solving tasks. Most recent work improves their performance through supervised fine-tuning (SFT) with labeled data or…
A LLM-Driven Multi-Agent Systems for Professional Development of Mathematics Teachers
Kaiqi Yang, Hang Li, Yucheng Chu +4
Professional development (PD) serves as the cornerstone for teacher tutors to grasp content knowledge. However, providing equitable and timely PD opportunities for teachers poses s…
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…
Beyond Partisan Leaning: A Comparative Analysis of Political Bias in Large Language Models
Tai-Quan Peng, Kaiqi Yang, Sanguk Lee +4
As large language models (LLMs) become increasingly embedded in civic, educational, and political information environments, concerns about their potential political bias have grown…