structured flexible and robust

数据集‘structured flexible and robust’由剑桥大学和麻省理工学院的研究团队创建，旨在评估和改进大型语言模型在分布外推理任务中的人类化行为。该数据集包含两个问题解决领域（规划和解释生成），并设计用于需要泛化到新语言表达的分布外问题的场景。数据集通过迭代约束生成范式，扩展初始语言提示，使用语言约束限制最常见的人类响应，迫使生成需要新颖语言生产的响应，从而促进更高程度的思考。该数据集的应用领域包括评估语言模型在复杂和非标准人类语言分布下的表现，以及探索混合AI模型在更人类化推理方面的潜力。

The dataset 'structured flexible and robust' was developed by a research team from the University of Cambridge and the Massachusetts Institute of Technology (MIT), aiming to evaluate and improve the human-like behaviors of Large Language Models (LLMs) in out-of-distribution (OOD) reasoning tasks. This dataset covers two problem-solving domains: planning and explanation generation, and is designed for out-of-distribution problem scenarios that require generalization to novel linguistic expressions. Adopting an iterative constraint generation paradigm, it expands initial language prompts, uses linguistic constraints to restrict the most common human responses, and forces the generation of responses that demand novel language production, thereby promoting a higher level of deliberate reasoning. The application areas of this dataset include evaluating the performance of language models under complex and non-standard human language distributions, as well as exploring the potential of hybrid AI models for more human-like reasoning.