![[2602.16039] How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16039] How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment
Summary
This article benchmarks various uncertainty metrics for LLM-based automatic assessment, highlighting the challenges of output uncertainty in educational contexts and offering insights for improving grading systems.
Why It Matters
As large language models (LLMs) become integral to educational assessment, understanding their uncertainty metrics is crucial. This research addresses the potential pitfalls of unreliable grading, which can adversely affect student learning outcomes and instructional decisions. By systematically evaluating uncertainty quantification methods, the study aims to enhance the reliability of automated grading systems.
Key Takeaways
- LLMs introduce significant output uncertainty in automatic assessments.
- Unreliable uncertainty estimates can disrupt educational interventions.
- The study benchmarks various uncertainty metrics in grading scenarios.
- Key factors influencing uncertainty include model families and assessment tasks.
- Findings aim to inform the development of more reliable grading systems.
Computer Science > Artificial Intelligence arXiv:2602.16039 (cs) [Submitted on 17 Feb 2026] Title:How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment Authors:Hang Li, Kaiqi Yang, Xianxuan Long, Fedor Filippov, Yucheng Chu, Yasemin Copur-Gencturk, Peng He, Cory Miller, Namsoo Shin, Joseph Krajcik, Hui Liu, Jiliang Tang View a PDF of the paper titled How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment, by Hang Li and 11 other authors View PDF HTML (experimental) Abstract:The rapid rise of large language models (LLMs) is reshaping the landscape of automatic assessment in education. While these systems demonstrate substantial advantages in adaptability to diverse question types and flexibility in output formats, they also introduce new challenges related to output uncertainty, stemming from the inherently probabilistic nature of LLMs. Output uncertainty is an inescapable challenge in automatic assessment, as assessment results often play a critical role in informing subsequent pedagogical actions, such as providing feedback to students or guiding instructional decisions. Unreliable or poorly calibrated uncertainty estimates can lead to unstable downstream interventions, potentially disrupting students' learning processes and resulting in unintended negative consequences. To systematically understand this challenge and inform future research, we benchmark a broad range of uncertainty qua...
