While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to reason robustly in complex settings. However, evaluating LLM reasoning is challenging because system capabilities continue to grow while benchmark datasets for tasks like logical deduction have remained static. We introduce MuSR, a dataset for evaluating language models on multistep soft reasoning tasks specified in a natural language narrative. This dataset has two crucial features. First, it is created through a novel neurosymbolic synthetic-to-natural generation algorithm, enabling the construction of complex reasoning instances that challenge GPT-4 (e.g., murder mysteries roughly 1000 words in length) and which can be scaled further as more capable LLMs are released. Second, our dataset instances are free text narratives corresponding to real-world domains of reasoning; this makes it simultaneously much more challenging than other synthetically-crafted benchmarks while remaining realistic and tractable for human annotators to solve with high accuracy. We evaluate a range of LLMs and prompting techniques on this dataset and characterize the gaps that remain for techniques like chain-of-thought to perform robust reasoning.

翻译：尽管配备思维链提示等技术的大型语言模型（LLMs）已展现出令人瞩目的能力，但在复杂环境下进行鲁棒推理方面仍有不足。然而，由于系统能力持续提升，而逻辑推理等任务的基准数据集保持静态，评估LLM的推理能力面临挑战。我们提出MuSR——一个用于评估语言模型在自然语言叙事中执行多步软推理任务的数据集。该数据集具有两个关键特征：首先，通过新颖的神经符号合成-自然生成算法构建，能生成挑战GPT-4的复杂推理实例（如约千词的谋杀谜案），并可在更强大的LLM发布时进一步扩展；其次，数据实例均为对应现实推理领域的自由文本叙事，这使其在保持人工标注者高精度求解可行性的同时，远比其它合成基准更具挑战性。我们在该数据集上评估了多种LLM和提示技术，并刻画了思维链等方法实现鲁棒推理尚存的差距。