IML Machine Learning Working Group: LLMs for HEP

Tuesday 9 Apr 2024, 15:00 → 18:00 Europe/Zurich

500/1-001 - Main Auditorium (CERN)

Topic: LLMs for HEP

15:00 → 15:05 News

Speakers: Anja Butter (Centre National de la Recherche Scientifique (FR)), Daniel Whiteson (University of California Irvine (US)), Fabio Catalano (CERN), Julian Garcia Pardinas (CERN), Lorenzo Moneta (CERN), Dr Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA)), Stefano Carrazza (CERN)

News_090424.pdf

15:05 → 15:20 AccGPT – A CERN Chatbot for Internal Knowledge Retrieval

Speaker: Dr Florian Rehm (CERN)

AccGPT-IML_v2.pdf

15:20 → 15:25 Discussion

15:25 → 15:40 Learning the language of QCD jets with transformers

Speakers: Dr Alexander Mück (RWTH Aachen University), Michael Kramer (Rheinisch Westfaelische Tech. Hoch. (DE))

mueck.pdf

15:40 → 15:45 Discussion

15:45 → 16:00 OmniJet-α: The first cross-task foundation model for particle physics

Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a major breakthrough as they could improve the achievable physics performance while at the same time drastically reduce the required amount of training time and data.
We report significant progress on this challenge on several fronts. First, a comprehensive set of evaluation methods is introduced to judge the quality of an encoding from physics data into a representation suitable for the autoregressive generation of particle jets with transformer architecture (the common backbone of foundation models). These measures motivate the choice of a higher-fidelity tokenization compared to previous works. Finally, we demonstrate transfer learning between an unsupervised problem (jet generation) and a classic supervised task (jet tagging) with our new OmniJet-α model. This is the first successful transfer between two different and actively studied classes of tasks and constitutes a major step in the building of foundation models for particle physics.

Speakers: Anna Maria Cecilia Hallin (University of Hamburg), Gregor Kasieczka (Hamburg University (DE)), Joschka Birk (Albert Ludwigs Universitaet Freiburg (DE))

OmniJet-alpha_A_Hallin.pdf

16:00 → 16:05 Discussion

16:05 → 16:20 ChATLAS: developing an AI assistant for the ATLAS collaboration

Speaker: Daniel Thomas Murnane (Lawrence Berkeley National Lab. (US))

chATLAS IML Meeting.pdf

16:20 → 16:25 Discussion