ChemRxivQuest: A Curated Chemistry Question-Answer Database Extracted from ChemRxiv Preprints
Mahmoud Amiri, Thomas Bocklitz·May 08, 2025
Summary
ChemRxivQuest, a 970-question, 155-answer dataset, supports chemistry-focused NLP, emphasizing conceptual, mechanistic, applied, and experimental questions. Constructed using an automated pipeline combining OCR, GPT-4o, and fuzzy matching, this dataset offers a foundational resource for NLP research, education, and tool development in chemistry. It addresses the challenges of efficient knowledge extraction in chemistry, where traditional search methods using keyword queries yield unstructured documents requiring manual review. Recent NLP advancements, particularly LLMs and RAG, offer new approaches to automate tasks like experimental workflows, data extraction, and novel compound discovery.
Introduction
Background
Overview of NLP in chemistry
Challenges in extracting knowledge from chemistry literature
Objective
To introduce ChemRxivQuest as a foundational resource for NLP in chemistry
Highlighting the dataset's role in addressing knowledge extraction challenges
Dataset Overview
Composition
Description of the 970-question, 155-answer structure
Explanation of the dataset's focus on conceptual, mechanistic, applied, and experimental questions
Construction
Automated pipeline combining OCR, GPT-4o, and fuzzy matching
Importance of this approach in creating a structured dataset from unstructured chemistry literature
Applications
Research
Utilization in NLP research for chemistry
Advancements in LLMs and RAG for chemistry-related tasks
Education
Role in chemistry education and learning
Enhancing understanding and application of chemical concepts
Tool Development
Support for developing NLP tools specifically for chemistry
Automation of tasks like experimental workflows, data extraction, and novel compound discovery
Challenges and Solutions
Challenges
Overview of challenges in chemistry-focused NLP
Limitations of traditional search methods using keyword queries
Solutions
Role of ChemRxivQuest in overcoming these challenges
How the dataset facilitates more efficient and structured knowledge extraction
Conclusion
Future Directions
Potential for further research and development
Expected advancements in NLP for chemistry
Impact
Expected impact on chemistry research, education, and tool development
Basic info
papers
artificial intelligence
Advanced features