Do LLMs fail in bridging generation?

Natalia Skachkova, Simon Ostermann, Josef van Genabith, Bernd Kiefer


Abstract
In this work we investigate whether large language models (LLMs) ‘understand’ bridging relations and can use this knowledge effectively. We present the results obtained from two tasks: generation of texts containing bridging and filling in missing bridging spans. We show that in most of the cases LLMs fail to generate bridging in a reliable way.
Anthology ID:
2025.jlcl-2.5
Volume:
Journal for Language Technology and Computational Linguistics, Vol. 38 No. 2: LLM Fails - Failed experiments with generative AI and what we can learn from them
Month:
July
Year:
2025
Address:
Editors:
Ngoc Duyen Tanja Tu, Annelen Brunner, Christian Lang
Venue:
JLCL
SIG:
Publisher:
German Society for Computational Linguistics and Language Technology
Note:
Pages:
77–95
Language:
URL:
https://aclanthology.org/2025.jlcl-2.5/
DOI:
10.21248/jlcl.38.2025.286
Bibkey:
Cite (ACL):
Natalia Skachkova, Simon Ostermann, Josef van Genabith, and Bernd Kiefer. 2025. Do LLMs fail in bridging generation?. Journal for Language Technology and Computational Linguistics, 38(2):77–95.
Cite (Informal):
Do LLMs fail in bridging generation? (Skachkova et al., JLCL 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.jlcl-2.5.pdf