AI Study Finds More One-Off Training Examples Raise Exact Science Matches
Photo: TokenPost
Language models trained with a higher share of examples seen only once produced more exact matches in materials science and gene-regulation tasks, raising questions about how researchers curate scientific training data. The experiments used OpenAI’s gpt-oss-20b and Google’s gemma-3-12b-it, both open-weight models. Researchers affiliated with Johns Hopkins University, Microsoft and Apple varied the proportion of one-time examples, which they called “monofacts.” In materials science, a monofact was a chemical system appearing once in the training set. In regulatory genomics, it was a DNA sequence with no other training example overlapping it by more than 150 nucleotides.
Source: TokenPost
