AMATI at BEA 2026 Shared Task 2: Automatic Short Answer Grading with Inductive Logic Programming and a Large Language Model
Year: 2026
Type: conference-paper
Abstract: We discuss the AMATI submission to the BEA 2026 Shared Task on Rubric-based Short Answer Scoring for German.Our neuro-symbolic system uses a combination of symbolic rules, automatically learned with a form of Inductive Logic Programming, and the Mistral-large language model.We wanted to investigate whether the combination would improve overall grading performance, while using the automatically induced symbolic rules for explainability, and the LLM for robustness.We find that the combination of approached resulted in improved overall performance for the 3-way task.However, including the symbolic rules did not improve upon Mistral's performance in the 2-way test.This paper presents our approach to the unseen answers challenges.Our team finished 6 th out of 9 in the 2-way challenge, and 5 th out of 8 in the 3-way challenge.In the 3-way challenge, neither our symbolic system nor the use of Mistral alone would have placed higher than 6 th of the 8 competitors, illustrating the improvement of the combined approach over either of the individual approaches.
Authors: Alistair Willis, Aisling Third
Institution: The Open University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166798692