An important aspect of developing LLMs that interact with humans is to align
models' behavior to their users. It is possible to prompt an LLM into behaving
as a certain persona, especially a user group or ideological persona the model
captured during its pertaining stage. But, how to best align an LLM with a
specific user and not a demographic or ideological group remains an open
question. Mining public opinion surveys (by Pew Research), we find that the
opinions of a user and their demographics and ideologies are not mutual
predictors. We use this insight to align LLMs by modeling both user opinions as
well as user demographics and ideology, achieving up to 7 points accuracy gains
in predicting public opinions from survey questions across a broad set of
topics. In addition to the typical approach of prompting LLMs with demographics
and ideology, we discover that utilizing the most relevant past opinions from
individual users enables the model to predict user opinions more accurately.

通过挖掘公众意见调查的数据，结合用户观点、人口统计学和意识形态信息来对 LLMs 进行定位，从而实现更好地预测各种话题下的公众观点，同时发现利用个体用户的相关过往意见可以提高预测准确性。