Frictionless knowledge injection for few-shot learning

Conference · Mon Dec 01 00:00:00 EST 2025 · Journal of Radioanalytical and Nuclear Chemistry

DOI:https://doi.org/10.1007/s10967-025-10548-7· OSTI ID:3017018

Stomps, Jordan ^[1]; Dayman, Ken J ^[1]; Randolph, Christian ^[1]; Hite, Jason ^[2]

---

1. ORNL
2. GTC Analytics

Cutting-edge machine learning methods often require large volumes of curated training data, precluding their use in national security problems with rare events in massive datasets. We present a method for incorporating abstract knowledge into models tailored for sparse data. A subject matter expert defines salient concepts using data examples, which are encoded in the model’s embedding space. Models are then trained to respect these concepts. This method enables knowledge injection, yielding effective models with limited labeled data and the ability to assess model sensitivity for subject matter expertise across the nonproliferation mission space, as demonstrated with Raman spectra analysis.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 3017018
• Resource Type: Conference paper/presentation
• Conference Information: Journal Name: Journal of Radioanalytical and Nuclear Chemistry Journal Volume: 334
• Country of Publication: United States
• Language: English