Navigating the landscape of particle accelerators has become increasingly
challenging with recent surges in contributions. These intricate devices
challenge comprehension, even within individual facilities. To address this, we
introduce PACuna, a fine-tuned language model refined through publicly
available accelerator resources like conferences, pre-prints, and books. We
automated data collection and question generation to minimize expert
involvement and make the data publicly available. PACuna demonstrates
proficiency in addressing intricate accelerator questions, validated by
experts. Our approach shows adapting language models to scientific domains by
fine-tuning technical texts and auto-generated corpora capturing the latest
developments can further produce pre-trained models to answer some intricate
questions that commercially available assistants cannot and can serve as
intelligent assistants for individual facilities.

通过精调的 PACuna 语言模型，我们在公开可获得的加速器资源上进行了改进，以应对越来越难理解的粒子加速器。我们的方法表明，通过对技术文本和自动生成的语料进行精调，可以进一步生成预训练模型，以回答一些商用助手无法解答的复杂问题，并可作为个别机构的智能助手。