  The alignment problem in the context of large language models must consider
the plurality of human values in our world. Whilst there are many resonant and
overlapping values amongst the world's cultures, there are also many
conflicting, yet equally valid, values. It is important to observe which
cultural values a model exhibits, particularly when there is a value conflict
between input prompts and generated outputs. We discuss how the co-creation of
language and cultural value impacts large language models (LLMs). We explore
the constitution of the training data for GPT-3 and compare that to the world's
language and internet access demographics, as well as to reported statistical
profiles of dominant values in some Nation-states. We stress tested GPT-3 with
a range of value-rich texts representing several languages and nations;
including some with values orthogonal to dominant US public opinion as reported
by the World Values Survey. We observed when values embedded in the input text
were mutated in the generated outputs and noted when these conflicting values
were more aligned with reported dominant US values. Our discussion of these
results uses a moral value pluralism (MVP) lens to better understand these
value mutations. Finally, we provide recommendations for how our work may
contribute to other current work in the field.
