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LongContext Reasoning Dataset

LongContext,Reasoning

This dataset is designed to tackle the core weaknesses of today's large language models when it comes to processing long documents and performing complex reasoning. It consists of 7,500 high-quality training examples across three languages—Chinese, English, and Korean. Each instance is built around a long-text passage and includes questions that require synthesizing information across paragraphs and documents, while following multi-step logical chains. The goal is to offer a thorough and rigorous evaluation framework that tests a model's ability to perceive long-range context, retrieve relevant information, construct sound reasoning paths, and trace evidence back to its source.

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SpecificationsSpecifications
Content
Long-document Multi-hop Reasoning QA Dataset
Data Size
7,500
Data Fields
id、context、file_count、question、answer、reasoning_chain、supporting_evidence、hops
Language
ZH,EN,KO
Format
JSON
Sample Sample
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