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Aligning language models (LMs) with human opinion is challenging yet vital toenhance their grasp of human values, preferences, and beliefs. We presentChOiRe, a four-step solution framework to predict human opinion thatdifferentiates between the user explicit personae (i.e. demographic orideological attributes) that are manually declared and implicit personaeinferred from user historical opinions. Specifically, it consists of (i) an LManalyzing 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 theexplicit personae and the most relevant implicit personae to perform opinionprediction; (iv) and where ChOiRe executes Step (iii) CoO multiple times withincreasingly larger lists of implicit personae to overcome insufficientpersonae information to infer a final result. ChOiRe achieves newstate-of-the-art effectiveness with limited inference calls, improving previousLLM-based techniques significantly by 3.22%.

ChOiRe: Characterizing and Predicting Human Opinions with Chain of Opinion Reasoning