随机思维森林：面向计算社会科学的感知不确定性推理
Random Forest-of-Thoughts: Uncertainty-aware Reasoning for Computational Social Science
AI总结：
针对社会调查分析中需要探索和不确定性搜索的复杂推理问题，提出随机思维森林（RFoT）提示方法，通过生成多样化思维空间并随机选择子思维构建思维森林，增强LLM决策能力，实验证明其在两个社会调查分析任务上显著提升性能。
摘要：
计算社会科学中的社会调查由详尽的领域理论精心设计，这些理论能有效反映受访者的深层想法，同时不掩盖其真实感受。候选问卷选项高度依赖于受访者之前的回答，这导致社会调查分析的复杂性、所需时间和专业知识要求较高。大型语言模型（LLM）通过提示学习（如思维链（CoT））能够增强复杂推理能力，但仍局限于从左到右的决策过程或推理过程中的有限路径。这意味着它们在需要探索和不确定性搜索的问题上可能表现不足。为此，提出了一种新颖的大型语言模型提示方法，称为随机思维森林（RFoT），用于生成不确定性推理，以适应计算社会科学领域。RFoT通过生成多样化的思维空间并随机选择子思维来构建思维森林，使LLM能够进行深思熟虑的决策。它可以扩展整体表现的探索和预测，得益于响应的广泛研究空间。该方法被应用于优化两个涵盖一系列社会调查分析问题的数据集上的计算社会科学分析。我们的实验表明，RFoT显著增强了语言模型在两个需要非平凡推理的新型社会调查分析问题上的能力。
英文摘要：
Social surveys in computational social science are well-designed by elaborate domain theories that can effectively reflect the interviewee's deep thoughts without concealing their true feelings. The candidate questionnaire options highly depend on the interviewee's previous answer, which results in the complexity of social survey analysis, the time, and the expertise required. The ability of large language models (LLMs) to perform complex reasoning is well-enhanced by prompting learning such as Chain-of-thought (CoT) but still confined to left-to-right decision-making processes or limited paths during inference. This means they can fall short in problems that require exploration and uncertainty searching. In response, a novel large language model prompting method, called Random Forest of Thoughts (RFoT), is proposed for generating uncertainty reasoning to fit the area of computational social science. The RFoT allows LLMs to perform deliberate decision-making by generating diverse thought space and randomly selecting the sub-thoughts to build the forest of thoughts. It can extend the exploration and prediction of overall performance, benefiting from the extensive research space of response. The method is applied to optimize computational social science analysis on two datasets covering a spectrum of social survey analysis problems. Our experiments show that RFoT significantly enhances language models' abilities on two novel social survey analysis problems requiring non-trivial reasoning.
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