Confidence Estimation in Automatic Short Answer Grading with LLMs
Automatic Short Answer Grading (ASAG) with generative large language models (LLMs) has recently demonstrated strong performance without task-specific fine-tuning, while also enabling the generation of synthetic feedback for educational assessment. Despite these advances, LLM-based grading remains imperfect, making reliable confidence estimates essential for safe and effective human-AI collaboration in educational decision-making. In this work, we investigate confidence estimation for ASAG with LLMs by jointly considering model-based confidence signals and dataset-derived uncertainty. We systematically compare three model-based confidence estimation strategies, namely verbalizing, latent, ...
| Authors | Longwei Cong, Sonja Hahn, Sebastian Gombert et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 April 2026 |
| Last updated at source | 13 May 2026 |
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