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：将语言模型与人类意见对齐是一项具有挑战性却至关重要的任务，有助于增强其对人类价值观、偏好及信念的掌握程度。我们提出ChOiRe——一个四步解决方案框架，用于预测人类意见，该框架区分了用户明确声明的显式人格特征（如人口统计或意识形态属性）与从用户历史意见中推断的隐式人格特征。具体而言，其包含：(i) 语言模型分析用户显式人格特征以筛选无关属性；(ii) 语言模型将隐式人格意见排序为偏好列表；(iii) 观点推理链推理，即语言模型依次分析显式人格特征及最相关的隐式人格特征以进行意见预测；(iv) ChOiRe通过逐步扩大隐式人格特征列表多次执行步骤(iii)的观点推理链，以克服人格特征信息不足的问题，最终推断出结果。ChOiRe在有限推理调用次数下实现了新的最优有效性，相较现有基于大语言模型的技术显著提升了3.22%。