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Evidence record 7123 · automatically gathered

Beyond Grading Accuracy: Exploring Alignment of TAs and LLMs

In this paper, we investigate the potential of open-source Large Language Models (LLMs) for grading Unified Modeling Language (UML) class diagrams. In contrast to existing work, which primarily evaluates proprietary LLMs, we focus on non-proprietary models, making our approach suitable for universities where transparency and cost are critical. Additionally, existing studies assess performance over complete diagrams rather than individual criteria, offering limited insight into how automated grad

Record details

Published: 17 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Transparency · Finance, VC & PE
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (17 March 2026), “Beyond Grading Accuracy: Exploring Alignment of TAs and LLMs,” evidence record 7123, https://ethics.ai/record/7123 (originally published by arXiv).

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