TRACK A: Large AI Models
The rise of large multimodal models like ChatGPT has significantly influenced both research and public perception of AI in recent months. Theme track A, “Large AI Models,” dives into the foundational technology and the latest breakthroughs of such large models. The track will feature labs covering essential and fundamental topics such as language modeling, multimodal models, and training with massive datasets. Additionally, it will explore more advanced themes, including LLM alignment and efficiency, along with application-specific topics like using LLMs for code generation. Attendees will gain comprehensive insights from leading experts through a combination of theoretical and practical sessions.
Monday, Sept 9, 2024
Opening Speech,
13:00-13:30
Keynote 1, 13:30-14:30
Prof. Dr. rer. nat. Dr. h.c. mult. Wolfgang Wahlster (DFKI)Professor Wolfgang Wahlster is a pioneer of AI in Germany and Europe as a founding director of the DFKI. He has served as an elected President of three international AI organizations: IJCAII, EurAI, and ACL. He is an elected Fellow of AAAI, EurAI, and GI. He laid some of the foundations for multimodal dialog systems, user modelling, and speech-to-speech translation cyber-physical production systems for the fourth industrial revolution (Industrie 4.0), a concept that he coined in 2010. Wahlster is a member of the Nobel Prize Academy in Stockholm, the German National Academy Leopoldina and three other prestigious academies. For his research, he has been awarded the German Future Prize, and the Grand Cross of Merit by the Federal President of Germany. (for more info see: https://www.wolfgang-wahlster.de/)
Industrial AI for Smart Manufacturing
Course 1, 15:00-17:30
Christophe Cerisara (CNRS)Christophe Cerisara is a French researcher at CNRS (National Centre for Scientific Research), specialized in machine learning models for natural language processing (NLP). He has created and is leading the SYNALP research team composed of about 20 NLP researchers since 2012. He is also the leader of the AI-NLP axis of the LORIA laboratory since 2019, and he has been referent for the French National Plan in AI in 2020. He has supervised more than 12 Ph.D. thesis, and has lead several projects about AI and training Large Language Models in the past few years.
Introduction to Large Language Models
Tuesday, Sept 10, 2024
Keynote 2, 9:00-10:00
Karën Fort (LORIA)Karën Fort is a Professor at Université de Lorraine and does her research at the LORIA laboratory in Nancy, in the Inria team Semagramme. Her primary research interest is ethics in natural language processing (NLP), of which she is a pioneer: she organized the first colloquium on the subject in 2014, in France, followed by a national workshop (ETeRNAL 2015 and 2020) and a special issue of the TAL journal in 2016. She initiated the ethics and NLP French blog (http://www.ethique-et-tal.org/) as well as the first survey on ethics in NLP (Fort & Couillault, 2016). She was co-chair of the first two ethics committees in the field (EMNLP 2020 and NAACL 2021) and is co-chair of the ethics committee of the association for computational linguistics (ACL). Beside her work on stereotypical biases (Névéol et al., 2022), she is interested in deontological ethics using NLP4NLP techniques (Abdalla et al, 2023).
Ethics in Natural Language Processing: don't look up!
Course 2, 10:30-13:00
Malte Ostendorff (Deutsche Telekom)Dr. Malte Ostendorff is a senior research engineer at Deutsche Telekom where he works on large language models (LLMs) and related topics. Previously, Malte was a senior researcher at the German Research Center for Artificial Intelligence (DFKI) and a Ph.D. student in the Scientific Information Analytics group at the University of Göttingen. Furthermore, Malte is a co-founder of Occiglot, a research collective for open-source language models for and by Europe, and a co-founder of Open Legal Data.
Training Data for Large Language Models
Course 3, 15:30-18:00
Christophe Cerisara (CNRS)Christophe Cerisara is a French researcher at CNRS (National Centre for Scientific Research), specialized in machine learning models for natural language processing (NLP). He has created and is leading the SYNALP research team composed of about 20 NLP researchers since 2012. He is also the leader of the AI-NLP axis of the LORIA laboratory since 2019, and he has been referent for the French National Plan in AI in 2020. He has supervised more than 12 Ph.D. thesis, and has lead several projects about AI and training Large Language Models in the past few years.
Efficient LLM training
The first part starts from discussing memory and computational costs of LLM, and covers concepts such as quantization, adapters, prefix tuning, qLoRA and model compression. The second part will briefly introduce notions related to transfer learning and continual learning: overfitting, learning to hallucinate, catastrophic forgetting, few-shot learning. The lab will focus on parameter-efficient methods.
The prerequisites for this course are the first introductory course about LLM and notions of machine learning.
I’d like to thank Pr. Simon Ostermann for providing the initial materials for this course.
Wednesday, Sept 11, 2024
Keynote 3, 9:00-10:00
TBAto be announced
Course 4, 10:30-13:00
Mariya Toneva (MPI)Mariya Toneva leads the Bridging AI and Neuroscience group (BrAIN) at the Max Planck Institute for Software Systems. Her research is at the intersection of Machine Learning, Natural Language Processing, and Neuroscience, with a focus on building computational models of language processing in the brain that can also improve natural language processing systems. She obtained her PhD from Carnegie Mellon University in a joint program between Machine Learning and Neural Computation.
Relating LLMs to human brains
The prerequisites are good familiarity with programming in python and basic machine learning concepts, such as regression and cross validation.
Course 5, 15:30-18:00
Jindong Gu (University of Oxford/Google DeepMind)Dr. Jindong Gu is a senior research fellow at University of Oxford. He also partially works in Google DeepMind as a faculty researcher in Gemini Safety team. Prior to that, He received his Ph.D. Degree from University of Munich. His research focus is to build Responsible AI systems. Specifically, he is interested in the interpretability, robustness, privacy, and safety of visual perception, foundation models, robotic policy and planning, and their fusion towards general intelligent systems.
Responsible Generative AI
Thursday, Sept 12, 2024
Keynote 4, 9:00-10:00
TBAto be announced
Course 6, 10:30-13:00
TBATBA
Course 7, 15:30-18:00
Gerrit Großmann (DFKI) Gerrit Großmann received his doctorate in Saarbrücken. His PhD topic was the behavior of stochastic processes on graphs and networks, including the spread of (online and offline) epidemics. He also worked within the interdisciplinary project NextAID, where he researched neuro-symbolic approaches for drug discovery, specifically by using diffusion models and graph neural networks.
Gerrit has been researching at DFKI in Saarbrücken and Kaiserslautern since 2023. His research interests there revolve around the question of how to integrate the distinct realms of discrete structures such as graphs and networks with the continuous nature of dynamic evolution, diffusion, and learning.
Language Models and Structured Knowledge in AI
Despite their groundbreaking impact, LLMs have their imperfections. This track examines the integration of LLMs with structured information like knowledge graphs. We investigate ways to improve the quality and reliability of LLMs and techniques for extracting structured data from them. By the end of the lab, you will have a first prototype of an implementation of an LLM combined with a knowledge graph. No specific experience working with LLMs is required, but some basic knowledge of deep learning is recommended.
Islam Mesabah obtained his Master’s degree in Computer Science from the RPTU (Rhineland-Palatinate Technical University) in Kaiserslautern. His master’s thesis focused on the application of Large Language Models (LLMs) for effective code generation through the utilization of API documentation. Additionally, he researched text-style transfer evaluation using LLMs.
Since 2023, Islam Mesabah has been serving as a researcher at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. His research at DFKI primarily explores the applications of Large Language Models and key information extraction and structuring from image documents. In addition to his research endeavors, Islam holds the position of teaching assistant for the “Engineering with Generative AI” course at RPTU Kaiserslautern.
Language Models and Structured Knowledge in AI
Despite their groundbreaking impact, LLMs have their imperfections. This track examines the integration of LLMs with structured information like knowledge graphs. We investigate ways to improve the quality and reliability of LLMs and techniques for extracting structured data from them. By the end of the lab, you will have a first prototype of an implementation of an LLM combined with a knowledge graph. No specific experience working with LLMs is required, but some basic knowledge of deep learning is recommended.
Friday, Sept 13, 2024
Course 8, 9:00-11:30
Alexandre Défossez (Kyutai)Alexandre is part of the founding research team at Kyutai, a leading non profit research lab in Paris. Before he was a research scientist for 3 years at Meta AI Research, leading in particular the development of the AudioCraft framework (EnCodec, AudioGen, MusicGen). Alexandre completed his PhD at Facebook AI Research and INRIA Paris, working in particular on music source separation (Demucs).
Auto-regressive modeling of discrete audio tokens.