@misc{10481/106277,
year = {2025},
month = {9},
url = {https://hdl.handle.net/10481/106277},
abstract = {In this article, I explored the application
of large language models (LLMs) in analysing lin-
guistic colexification and ambiguity within bioethical
scenarios. By employing word embeddings derived
from LLMs, I constructed semantic distance matrices
that provide insight into the relationships between key
terms in bioethical vignettes. These matrices were
used to quantify and visualize the degree of linguis-
tic ambiguity and specificity across different ver-
sions of each vignette—those with high colexification
(ambiguous language) and those with low colexifica-
tion (specific language). The approach taken involves
encoding words according to their semantic adja-
cency and representing these relationships geometri-
cally through distance matrices. The resulting matri-
ces reflect the nuanced differences in how concepts
are related within bioethical contexts, offering a quan-
titative method for analysing language use. The study
demonstrates that LLMs, by facilitating geometric
representations of language, can enhance our under-
standing of complex ethical dilemmas by systemati-
cally addressing linguistic ambiguity. Ultimately, this
research contributes to the field of bioethics by pro-
viding a computational approach to improving clarity
in ethical communication, highlighting the potential
of LLMs to inform both ethical decision-making and
discourse analysis. LLMs, while not capable of per-
forming speech acts in the full philosophical sense—
as human beings do—still serve as powerful tools to
analyse and understand bioethical language. This dis-
tinction—between performing speech acts and ana-
lysing their linguistic features—highlights the unique
contribution of LLMs as analytical tools rather than
ethical agents.},
publisher = {Springer Nature},
title = {The Geometry of Language: Understanding LLMs in Bioethics},
doi = {https://doi.org/10.1007/s11673-025-10480-1},
author = {Monasterio Astobiza, Aníbal},
}