Toward cultural interpretability: A linguistic anthropological framework for describing and evaluating large language models
Name
jones-et-al-2025-toward-cultural-interpretability-a-linguistic-anthropological-framework-for-describing-and-evaluating.pdf
Description
Published version
Size
549.71 KB
Format
Adobe PDF
Checksum (MD5)
1b5430d342b7c3c43dca73c91307bc70
Author(s)
Jones, Graham M
Satran, Shai
Satyanarayan, Arvind
Date Issued
March 2025
Journal
Big Data & Society
Publisher
SAGE Publications
Citation
Jones, G. M., Satran, S., & Satyanarayan, A. (2025). Toward cultural interpretability: A linguistic anthropological framework for describing and evaluating large language models. Big Data & Society, 12(1).
Version
Final published version
Abstract
This article proposes a new integration of linguistic anthropology and machine learning (ML) around convergent interests in both the underpinnings of language and making language technologies more socially responsible. While linguistic anthropology focuses on interpreting the cultural basis for human language use, the ML field of interpretability is concerned with uncovering the patterns that Large Language Models (LLMs) learn from human verbal behavior. Through the analysis of a conversation between a human user and an LLM-powered chatbot, we demonstrate the theoretical feasibility of a new, conjoint field of inquiry, cultural interpretability (CI). By focusing attention on the communicative competence involved in the way human users and AI chatbots coproduce meaning in the articulatory interface of human-computer interaction, CI emphasizes how the dynamic relationship between language and culture makes contextually sensitive, open-ended conversation possible. We suggest that, by examining how LLMs internally “represent” relationships between language and culture, CI can: (1) provide insight into long-standing linguistic anthropological questions about the patterning of those relationships; and (2) aid model developers and interface designers in improving value alignment between language models and stylistically diverse speakers and culturally diverse speech communities. Our discussion proposes three critical research axes: relativity, variation, and indexicality.
MIT Department
MIT Anthropology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/20539517241303118
