TOPIK Reading Passage Semantic Reasoning Dataset
#semantic reasoning #reading comprehension #evidence-based question answering #TOPIK reading preparation #Korean document understanding #Korean reading comprehension
- 5000
- 0.3GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-10-19
AI Analysis & Value Prop
Designed for TOPIK Korean reading exam scenarios, this dataset provides structured Korean passages, questions, answer choices, correct answers, semantic judgments, explanations, and supporting evidence sentences. It covers main-idea identification, information retrieval, contextual word meaning, inter-sentence logic, and implied intent, supporting the development and improvement of Korean document understanding models. Its JSON input-output structure is suitable for domain-specific supervised fine-tuning, model evaluation, and answer traceability.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| explanation | string | Explains how the correct answer is derived from the passage, context, and relationships between sentences. |
| input_record | object | Contains the original Korean reading passage, question, answer choices, question type, and other information to be analyzed. |
| correct_option | string | The option number or label identified as correct through semantic reasoning about the passage. |
| semantic_judgment | string | A consolidated judgment of the semantic relationship between the passage, question, and correct option. |
| evidence_sentences | array | A list of original passage sentences or key statements that support the correct answer. |
| correct_answer_text | string | The full answer text corresponding to the correct option. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What does the dataset contain?
- Each record includes a Korean reading passage, a question and answer choices, along with the correct option, answer text, semantic judgment, explanation, and evidence sentences. Questions cover skills such as identifying main ideas, locating information, interpreting words in context, reasoning about sentence relationships, and inferring implied intent.
- Which training and evaluation tasks does it support?
- The data can support TOPIK reading question answering, passage understanding, main idea identification, information retrieval, contextual word reasoning, and logical reasoning. It is also suitable for domain-specific supervised fine-tuning and capability evaluation of Korean education models.
- What is the data format, and how can it be obtained?
- Records are organized as JSON fields containing the original reading question input and structured answers and explanations. Check the product page for samples, delivery specifications, and available acquisition options, then configure your import workflow to match the data fields.
- What are the licensing and usage terms?
- Licensing scope, redistribution rights, and commercial use terms are governed by the license stated on the product page. Before using the data for training, evaluation, or product development, confirm the applicable terms and ensure that the content meets your intended use requirements.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={TOPIK Reading Passage Semantic Reasoning Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/af312dee86474a797d7bb73d85b49264},
urldate={2026-10-16},
keywords={TOPIK reading dataset,Korean reading comprehension,Korean exam training data,passage semantic reasoning,TOPIK question data,Korean document understanding,reading question answering,Korean model fine-tuning},
version={1.0}
}Using this in research? Please cite us.
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