arXiv:2505.05232v3 Announce Type: replace Abstract: The rapid expansion of chemistry literature poses significant challenges for researchers seeking to efficiently access domain-specific knowledge. To support advancements in chemistry-focused natural language processing (NLP), we present ChemQuests, a curated dataset of 952 high-quality question-answer (QA) pairs derived from 155 ChemRxiv \cite{chemrxivWebsite} papers across 17 subfields of chemistry. Each QA pair is explicitly linked to its source text segment to ensure traceability and contextual accuracy. ChemQuests was constructed using an
The rapid expansion of scientific literature makes it increasingly difficult for researchers to keep up, necessitating AI-powered tools for knowledge extraction.
A high-quality, domain-specific chemistry QA dataset can significantly accelerate the development of specialized AI models, improving efficiency and discovery in the chemical sciences.
The availability of ChemQuests provides a structured resource for training advanced chemistry-focused NLP systems, potentially transforming how chemical knowledge is accessed and utilized.
- · AI researchers (NLP)
- · Pharmaceutical companies
- · Material science companies
- · Academic chemistry departments
- · Manual literature review processes
- · Legacy chemistry information systems
Improved performance of chemistry-specific large language models and question-answering systems.
Faster hypothesis generation and experimental design in chemical research and development.
Accelerated discovery of new materials, drugs, and chemical processes, leading to economic and scientific breakthroughs.
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Read at arXiv cs.AI