DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
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 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.
Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 33 of its 46 references that are in the map. First posted on arXiv 4 Sep 2024.
Builds on
33 of 46 references.
Closest ancestors · 3
Same direction, newest first
- 2024BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack14 Jun
- 2024Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey2 Apr
- 2022True Detective: A Deep Abductive Reasoning Benchmark Undoable for GPT-3 and Challenging for GPT-420 Dec
Other references · 30
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- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
- 2023Qwen Technical Reportcited by 3,273
- 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
- 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,044
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
- 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,378
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- 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
- 2020Long Range Arena: A Benchmark for Efficient Transformerscited by 696
- 2023C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Modelscited by 694
- 2017The NarrativeQA Reading Comprehension Challengecited by 590
- 2019ELI5: Long Form Question Answeringcited by 567
- 2024∞Bench: Extending Long Context Evaluation Beyond 100K Tokenscited by 360
- 2021Recursively Summarizing Books with Human Feedbackcited by 287
- 2023L-Eval: Instituting Standardized Evaluation for Long Context Language Modelscited by 232
- 2021QuALITY: Question Answering with Long Input Texts, Yes!cited by 204
- 2023LooGLE: Can Long-Context Language Models Understand Long Contexts?cited by 200
- 2023ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understandingcited by 175
- 2022SCROLLS: Standardized CompaRison Over Long Language Sequencescited by 170
- 2023Large Language Models for Robotics: A Surveycited by 132
- 2023Retrieval meets Long Context Large Language Modelscited by 114
- 2024One Thousand and One Pairs: A "novel" challenge for long-context language modelscited by 80
- 2024Evaluating Mathematical Reasoning Beyond Accuracycited by 69
- 2024In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Misscited by 36
- 2022CAB: Comprehensive Attention Benchmarking on Long Sequence Modelingcited by 10
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Citing papers per year: 2025: 8; 2026: 13.
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