Towards Measuring the Representation of Subjective Global Opinions in
  Language Models
Authors: name: Esin Durmus, name: Karina Nyugen, name: Thomas I. Liao, name: Nicholas Schiefer, name: Amanda Askell, name: Anton Bakhtin, name: Carol Chen, name: Zac Hatfield-Dodds, name: Danny Hernandez, name: Nicholas Joseph, name: Liane Lovitt, name: Sam McCandlish, name: Orowa Sikder, name: Alex Tamkin, name: Janel Thamkul, name: Jared Kaplan, name: Jack Clark, name: Deep Ganguli

Large language models (LLMs) may not equitably represent diverse global
perspectives on societal issues. In this paper, we develop a quantitative
framework to evaluate whose opinions model-generated responses are more similar
to. We first build a dataset, GlobalOpinionQA, comprised of questions and
answers from cross-national surveys designed to capture diverse opinions on
global issues across different countries. Next, we define a metric that
quantifies the similarity between LLM-generated survey responses and human
responses, conditioned on country. With our framework, we run three experiments
on an LLM trained to be helpful, honest, and harmless with Constitutional AI.
By default, LLM responses tend to be more similar to the opinions of certain
populations, such as those from the USA, and some European and South American
countries, highlighting the potential for biases. When we prompt the model to
consider a particular country's perspective, responses shift to be more similar
to the opinions of the prompted populations, but can reflect harmful cultural
stereotypes. When we translate GlobalOpinionQA questions to a target language,
the model's responses do not necessarily become the most similar to the
opinions of speakers of those languages. We release our dataset for others to
use and build on. Our data is at
https://huggingface.co/datasets/Anthropic/llm_global_opinions. We also provide
an interactive visualization at https://llmglobalvalues.anthropic.com.