SDPA at BEA 2026 Shared Task 2: Efficient LLM Fine-Tuning for Rubric-based Short Answer Scoring
Year: 2026
Type: conference-paper
Abstract: Automated short-answer scoring (ASA) is an important yet challenging task in educational assessment as it aims to evaluate open-ended student responses against predefined scoring rubrics that are often interrelated.Although large language models (LLMs) have demonstrated impressive capabilities in text understanding and reasoning, their application to ASA has primarily focused on prompt-based inference, largely due to the limited availability of annotated data required for effective model training.In this work, we investigate parameterefficient fine-tuning strategies for LLMs using ASA annotations in German.Our experiments show that fine-tuned LLMs consistently outperform both prompt-based and ensemble-based language models, suggesting domain-adaptive LLM fine-tuning is more effective than prompting alone for ASA.
Authors: Zhexiong Liu, Jing Zhang
Institutions: University of Pittsburgh, Emory University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166840642