Speaker
Description
In my talk, I would like to discuss the role of large language models (LLMs) in supporting GW candidate validation during O5 observing run, in complement to shifts rota that were in place to provide reliability to the follow-up and mitigate the risk to consume telescope ressources for no reason. While human-in-the-loop validation has remained critical over the last campaigns, we are raising new challenges with the increasing rates of alerts and their diversity. It was also hard to maintain during O4 a certain number of participation in the O4 operations. In my talk, I will explore pro/cons LLM-based framework trained on past operational knowledge, metadata, and expert interactions to assist operations in the alert dissemination and reduce the shift workload. I will also enumerate the key ingredients to make it come true which also include the connection of data science (that can be related to same problematics than CERN for operation in data analysis).