DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels

• URL: http://arxiv.org/abs/2409.02465v2
• Date: Fri, 14 Mar 2025 08:44:06 GMT
• Title: DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels
• Authors: Zhe Xu, Jiasheng Ye, Xiaoran Liu, Xiangyang Liu, Tianxiang Sun, Zhigeng Liu, Qipeng Guo, Linlin Li, Qun Liu, Xuanjing Huang, Xipeng Qiu,
• Abstract summary: We propose textbfDetectiveQA, a dataset specifically designed for narrative reasoning within long contexts.<n>We leverage detective novels, averaging over 100k tokens, to create a dataset containing 1200 human-annotated questions in both Chinese and English.
• Score: 86.93099925711388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, significant efforts have been devoted to enhancing the long-context capabilities of Large Language Models (LLMs), particularly in long-context reasoning. To facilitate this research, we propose \textbf{DetectiveQA}, a dataset specifically designed for narrative reasoning within long contexts. We leverage detective novels, averaging over 100k tokens, to create a dataset containing 1200 human-annotated questions in both Chinese and English, each paired with corresponding reference reasoning steps. Furthermore, we introduce a step-wise reasoning metric, which enhances the evaluation of LLMs' reasoning processes. We validate our approach and evaluate the mainstream LLMs, including GPT-4, Claude, and LLaMA, revealing persistent long-context reasoning challenges and demonstrating their evidence-retrieval challenges. Our findings offer valuable insights into the study of long-context reasoning and lay the base for more rigorous evaluations.

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