  Aligning language models (LMs) with human opinion is challenging yet vital to
enhance their grasp of human values, preferences, and beliefs. We present
ChOiRe, a four-step framework to predict human opinion which differentially
models the user explicit personae (i.e. demographic or ideological attributes)
that are manually declared, and implicit personae inferred from user historical
opinions. ChOiRe consists of (i) an LM analyzing the user explicit personae to
filter out irrelevant attributes; (ii) the LM ranking the implicit persona
opinions into a preferential list; (iii) Chain-of-Opinion (CoO) reasoning,
where the LM sequentially analyzes the explicit personae and the most relevant
implicit personae to perform opinion prediction; (iv) and where ChOiRe executes
Step (iii) CoO multiple times with increasingly larger lists of implicit
personae to overcome insufficient personae information to infer a final result.
ChOiRe achieves new state-of-the-art effectiveness with limited inference
calls, improving previous techniques significantly by 3.22%. We also show that
ChOiRe Steps (i) and (ii) can significantly better fine-tune opinion-aligned
models, by up to 18.44%.
