@misc{kucera2026scidefautomatingdefinitionextraction,

title = {SciDef: Automating Definition Extraction from Academic Literature with Large Language Models},

author = {Filip Kučera and Christoph Mandl and Isao Echizen and Radu Timofte and Timo Spinde},

url = {https://arxiv.org/abs/2602.05413},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

abstract = {Definitions are the foundation for any scientific work, but with a significant increase in publication numbers, gathering definitions relevant to any keyword has become challenging. We therefore introduce SciDef, an LLM-based pipeline for automated definition extraction. We test SciDef on DefExtra & DefSim, novel datasets of human-extracted definitions and definition-pairs' similarity, respectively. Evaluating 16 language models across prompting strategies, we demonstrate that multi-step and DSPy-optimized prompting improve extraction performance. To evaluate extraction, we test various metrics and show that an NLI-based method yields the most reliable results. We show that LLMs are largely able to extract definitions from scientific literature (86.4% of definitions from our test-set); yet future work should focus not just on finding definitions, but on identifying relevant ones, as models tend to over-generate them.



Code & datasets are available at https://github.com/Media-Bias-Group/SciDef.},

howpublished = {arXiv (preprint)},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}