ArticleJournal of chemical information and modeling2025
Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models.Journal of chemical information and modeling · 2026Article
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Authors and funding
2 authors.
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Abstract
We present a method for autogenerating a large domain-specific question-answering (QA) dataset from a thermoelectric materials database. We show that a small language model, BERT, once fine-tuned on this automatically generated dataset of 99,757 QA pairs about thermoelectric materials, affords better performance in the field of thermoelectric materials compared to a BERT model fine-tuned on the generic English-language QA data set, SQuAD-v2. We further show that mixing the two data sets (ours and SQuAD-v2), which have significantly different syntactic and semantic scopes, allows the BERT model to achieve even better performance. The best-performing BERT model fine-tuned on the mixed data set outperforms the models fine-tuned on the other two data sets by scoring an exact match of 67.93% and an
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Registered trials
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