Detectives frequently engage in information detection and reasoning simultaneously when making decisions across various cases, especially when confronted with a vast amount of information. With the rapid development of large language models~(LLMs), evaluating how these models identify key information and reason to solve questions becomes increasingly relevant. We introduces the DetectBench, a reading comprehension dataset designed to assess a model's ability to jointly ability in key information detection and multi-hop reasoning when facing complex and implicit information. The DetectBench comprises 3,928 questions, each paired with a paragraph averaging 190 tokens in length. To enhance model's detective skills, we propose the Detective Thinking Framework. These methods encourage models to identify all possible clues within the context before reasoning. Our experiments reveal that existing models perform poorly in both information detection and multi-hop reasoning. However, the Detective Thinking Framework approach alleviates this issue.

翻译：侦探在处理各类案件时，常常需要同时进行信息检测与推理，尤其是在面对海量信息时。随着大语言模型（LLMs）的快速发展，评估这些模型如何识别关键信息并进行推理解决问题变得日益重要。我们引入了DetectBench——一个阅读理解数据集，旨在评估模型在面对复杂隐晦信息时，同时进行关键信息检测与多步推理的能力。DetectBench包含3,928个问题，每个问题配有一段平均长度为190个token的段落。为提升模型的侦探能力，我们提出了侦探思维框架（Detective Thinking Framework）。这些方法鼓励模型在推理前识别上下文中的所有可能线索。实验表明，现有模型在信息检测与多步推理方面表现欠佳，而侦探思维框架方法则缓解了这一问题。