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Description
Recent advances in language models have sparked growing interest in their use for specialized scientific applications. In this work, we apply supervised fine-tuning to adapt a small pretrained language model to a task in theoretical particle physics. We consider the generation of particle-physics Lagrangians from structured field descriptions. Given a set of fields, the model must generate the corresponding Lagrangian with the required objects and contractions.
On a separate evaluation dataset, more than 95% of the generated Lagrangians fully matched the reference expressions in terms of the required objects and contractions. Additional experiments with different model sizes and prompting strategies showed that increasing the number of model parameters did not necessarily improve performance and that the results were sensitive to how the task was presented in the prompt.
Our results show that a language model with three billion parameters can accurately learn the mapping from field descriptions to the corresponding Lagrangians. Whether the model captures general physical principles or primarily exploits statistical regularities in the chosen representation remains an open question.