The performance and usability of Large-Language Models (LLMs) are driving their use in explanation generation tasks. However, despite their widespread adoption, LLM explanations have been found to be unreliable, making it difficult for users to distinguish good from bad explanations. To address this issue, we present Rubrik’s CUBE–an education-inspired rubric and a dataset of 26k explanations, written and later quality-annotated using the rubric by both humans and six open- and closed-source LLMs. The CUBE dataset focuses on two reasoning and two language tasks, providing the necessary diversity for us to effectively test our proposed rubric. Using Rubrik, we find that explanations are influenced by both task and perceived difficulty. Low quality stems primarily from a lack of conciseness in LLM-generated explanations, rather than cohesion and word choice. The full dataset, rubric, and code are available at https://github.com/RubriksCube/rubriks_cube.

Originally developed to reduce the manual burden of grading standardised language tests, Automated Essay Scoring (AES) research has long focused on holistic scoring methods which offer minimal formative feedback in the classroom. With the increasing demand for technological tools that support language acquisition, the field is turning to analytic AES (evaluating essays according to different linguistic traits). This approach holds promise for generating more detailed essay feedback, but relies on analytic scoring data that is both more cognitively demanding for humans to produce, and prone to bias. The dominant paradigm in AES is to aggregate disagreements between raters into a single gold-standard label, which fails to account for genuine examiner variability. In an attempt to make AES more representative and trustworthy, we propose to explore the sources of disagreements and lay out a novel AES system design that learns from individual raters instead of the gold standard labels.