Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset
This addresses the issue of distinguishing good from bad LLM explanations for users, though it is incremental as it builds on existing evaluation methods.
The paper tackles the problem of unreliable LLM explanations by introducing Rubrik's CUBE, a rubric and dataset of 26k explanations, finding that low quality is primarily due to lack of conciseness rather than cohesion and word choice.
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.