Work

Domain-specific text embedding model for accelerator physics

Year: 2025

Type: article

Abstract: Accelerator physics presents unique challenges for natural language processing (NLP) due to its specialized terminology and complex concepts. A key component in overcoming these challenges is the development of robust text embedding models that transform textual data into dense vector representations, facilitating efficient information retrieval and semantic understanding. In this work, we introduce AccPhysBERT, a sentence embedding model fine-tuned specifically for accelerator physics. Our model demonstrates superior performance across a range of downstream NLP tasks, surpassing existing models in capturing the domain-specific nuances of the field. We further showcase its practical applications, including semantic paper-reviewer matching and integration into retrieval-augmented generation systems, highlighting its potential to enhance information retrieval and knowledge discovery in accelerator physics.

Source: Physical Review Accelerators and Beams

DOI: 10.1103/physrevaccelbeams.28.044601

Authors: Thorsten Hellert, João Montenegro, Marco Venturini, Andrea Pollastro

Institutions: Lawrence Berkeley National Laboratory, University of Naples Federico II

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: U.S. Department of Energy, National Energy Research Scientific Computing Center, Office of Science