The Geometry of Language: Understanding LLMs in Bioethics

Area of Science:

• Computational Linguistics
• Bioethics
• Natural Language Processing

Background:

• Bioethical scenarios often involve complex language with potential for ambiguity.
• Understanding linguistic nuances is crucial for effective ethical decision-making and discourse.

Purpose of the Study:

• To explore the application of large language models (LLMs) in analyzing linguistic colexification and ambiguity in bioethics.
• To develop a quantitative method for assessing language specificity and ambiguity in bioethical texts.

Main Methods:

• Utilized word embeddings from LLMs to construct semantic distance matrices.
• Quantified and visualized linguistic ambiguity by comparing vignettes with high and low colexification.
• Represented semantic relationships geometrically through distance matrices.

Main Results:

• Semantic distance matrices effectively revealed nuanced term relationships within bioethical contexts.
• The LLM-based approach provided a quantitative measure of linguistic ambiguity and specificity.
• Demonstrated LLMs' capability to facilitate geometric language representations for analysis.

Conclusions:

• LLMs offer a powerful computational tool for analyzing bioethical language and reducing ambiguity.
• This method enhances understanding of complex ethical dilemmas and improves clarity in ethical communication.
• LLMs serve as valuable analytical tools for discourse analysis in bioethics, distinct from performing speech acts.