Nexdata-AI/Long-Context-Reasoning-Data-Sample

Long-Context-Reasoning-Dataset

Description

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.

For more details, please refer to the link: https://www.nexdata.ai/datasets/llm/2121?source=Huggingface

Specifications

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

Full Dataset Access

The complete dataset is available upon request. Reach out to us to learn more and submit an access request.