Piecing Together Clues: A Benchmark for Evaluating the Detective Skills of Large Language Models
Zhouhong Gu, Lin Zhang, Jiangjie Chen, Haoning Ye, Xiaoxuan Zhu, Zihan Li, Zheyu Ye, Yan Gao, Yao Hu, Yanghua Xiao, Hongwei Feng
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.