Fuzzy Semantic Retrieval Strategies for Automated Short-Answer Grading with Large Language Models in Language Learning

Thomas Moerman; Jasper Degraeuwe; Arda Tezcan

Fuzzy Semantic Retrieval Strategies for Automated Short-Answer Grading with Large Language Models in Language Learning

Computational Linguistics in the Netherlands Journal, vol. 15, 2026

Educational NLP

Adapts translation-memory fuzzy matching to automated grading, with an accuracy and recall trade-off by shot count.

Authors

Thomas Moerman

Jasper Degraeuwe

Arda Tezcan

Published

January 1, 2026

This work adapts translation-memory fuzzy matching to automated grading of short-answer language exercises. It evaluates ten retrieval strategies across 306 experiments and finds an accuracy and recall trade-off governed by the number of examples, with semantic retrieval generally beating non-semantic strategies.

Research theme: Educational NLP

Citation

BibTeX citation:

@article{moerman2026,
  author = {Moerman, Thomas and Degraeuwe, Jasper and Tezcan, Arda},
  title = {Fuzzy {Semantic} {Retrieval} {Strategies} for {Automated}
    {Short-Answer} {Grading} with {Large} {Language} {Models} in
    {Language} {Learning}},
  journal = {Computational Linguistics in the Netherlands Journal},
  date = {2026-01-01},
  langid = {en}
}

For attribution, please cite this work as:

Moerman, Thomas, Jasper Degraeuwe, and Arda Tezcan. 2026. “Fuzzy Semantic Retrieval Strategies for Automated Short-Answer Grading with Large Language Models in Language Learning.” Computational Linguistics in the Netherlands Journal, accepted, January 1.