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 solution framework to predict human opinion that differentiates between the user explicit personae (i.e. demographic or ideological attributes) that are manually declared and implicit personae inferred from user historical opinions. Specifically, it 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 LLM-based techniques significantly by 3.22%.

翻译：使语言模型与人类观点对齐虽具挑战，但对于提升其对人类价值观、偏好和信念的理解至关重要。我们提出ChOiRe——一种四阶段解决方案框架，通过区分用户手动声明的显性人物画像（如人口统计或意识形态属性）与从用户历史观点中推断的隐性人物画像，实现人类观点预测。具体而言，该框架包括：（i）语言模型分析用户显性人物画像，过滤无关属性；（ii）语言模型将隐性人物画像观点按偏好排序；（iii）观点推理链机制：语言模型依次分析显性人物画像及最相关的隐性人物画像，执行观点预测；（iv）ChOiRe通过逐步扩展隐性人物画像列表，多次执行第(iii)步的CoO推理，以克服画像信息不足的问题，最终推断结果。ChOiRe在有限推理调用次数下实现了新的最优效果，相比现有基于大语言模型的技术显著提升3.22%。