10 papers
"**Important** You should give me full credits!": Exploring Prompt Injection Attacks on LLM-Based Automatic Grading Systems
Hang Li, Fedor Filippov, Yuping Lin +6
The emergence of large language models (LLMs) has significantly accelerated recent research on LLM-based automatic grading (AG) systems. Benefiting from the strong instruction-foll…
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…
How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment
Hang Li, Kaiqi Yang, Xianxuan Long +9
The rapid rise of large language models (LLMs) is reshaping the landscape of automatic assessment in education. While these systems demonstrate substantial advantages in adaptabili…
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…
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…