  Recent work in large language modeling (LLMs) has used fine-tuning to align
outputs with the preferences of a prototypical user. This work assumes that
human preferences are static and homogeneous across individuals, so that
aligning to a a single "generic" user will confer more general alignment. Here,
we embrace the heterogeneity of human preferences to consider a different
challenge: how might a machine help people with diverse views find agreement?
We fine-tune a 70 billion parameter LLM to generate statements that maximize
the expected approval for a group of people with potentially diverse opinions.
Human participants provide written opinions on thousands of questions touching
on moral and political issues (e.g., "should we raise taxes on the rich?"), and
rate the LLM's generated candidate consensus statements for agreement and
quality. A reward model is then trained to predict individual preferences,
enabling it to quantify and rank consensus statements in terms of their appeal
to the overall group, defined according to different aggregation (social
welfare) functions. The model produces consensus statements that are preferred
by human users over those from prompted LLMs (>70%) and significantly
outperforms a tight fine-tuned baseline that lacks the final ranking step.
Further, our best model's consensus statements are preferred over the best
human-generated opinions (>65%). We find that when we silently constructed
consensus statements from only a subset of group members, those who were
excluded were more likely to dissent, revealing the sensitivity of the
consensus to individual contributions. These results highlight the potential to
use LLMs to help groups of humans align their values with one another.
