GAIA: A General AI Assistant for Intelligent Accelerator Operations (2405.01359v1)
Abstract: Large-scale machines like particle accelerators are usually run by a team of experienced operators. In case of a particle accelerator, these operators possess suitable background knowledge on both accelerator physics and the technology comprising the machine. Due to the complexity of the machine, particular subsystems of the machine are taken care of by experts, who the operators can turn to. In this work the reasoning and action (ReAct) prompting paradigm is used to couple an open-weights LLM with a high-level machine control system framework and other tools, e.g. the electronic logbook or machine design documentation. By doing so, a multi-expert retrieval augmented generation (RAG) system is implemented, which assists operators in knowledge retrieval tasks, interacts with the machine directly if needed, or writes high level control system scripts. This consolidation of expert knowledge and machine interaction can simplify and speed up machine operation tasks for both new and experienced human operators.
- ReAct: Synergizing Reasoning and Acting in Language Models, March 2023. URL http://arxiv.org/abs/2210.03629. arXiv:2210.03629 [cs].
- Mixtral of Experts. 2024. doi:10.48550/ARXIV.2401.04088. URL https://arxiv.org/abs/2401.04088. Publisher: [object Object] Version Number: 1.
- Emergent Abilities of Large Language Models, October 2022. URL http://arxiv.org/abs/2206.07682. arXiv:2206.07682 [cs].
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, January 2023. URL http://arxiv.org/abs/2201.11903. arXiv:2201.11903 [cs].
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, April 2021. URL http://arxiv.org/abs/2005.11401. arXiv:2005.11401 [cs].