From Theory to Practice: Workshop on Large Language and Foundation Models (WLLFM'24)

Schedule

News

• [02 Dec 24] The schedule has been published.

• [28 Nov 24] The date for our workshop has been published: It is going to be the 16th of December (Monday). See also here: https://www3.cs.stonybrook.edu/~ieeebigdata2024/developing_schedule.html 

• [15 Nov 24]  The review process is complete and we thank all reviewers for their work. The notifications will be sent out today.

• [15 Nov 24] Exact workshop date will be announced as soon as the registration ends.

• [24 Oct 24] Submission link and information about the formatting instructions have been updated.

• [24 May 24] After a very successful first iteration, we are happy to inform you that the workshop on Large Language and Foundation Models will be hosted in conjunction with the IEEE BigData Conference 2024 in Washington DC this year! This year's workshop will be organized by Rafet Sifa (Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn and Fraunhofer IAIS Germany), Dhaval Patel (IBM Research US), Linsey Pang (Salesforce US), Wei Liu (University of Technology Sydney Australia) and Tobias Deußer (Fraunhofer IAIS Germany). 

About the workshop

Large language and foundation models have had a significant impact on the field of Artificial Intelligence. These models excel at complex language-related tasks like text generation, sentiment analysis, machine translation, and question answering, often surpassing human performance. However, there is still a gap between theoretical understanding and practical implementation. To bridge this gap, our workshop brings together researchers, practitioners, and industry experts to share experiences, insights, and best practices in leveraging these models. The workshop aims to foster collaboration between academia and industry, providing a platform to discuss advancements, address challenges, and explore opportunities in the field of foundation models and large language models. Additionally, this year's workshop will feature keynote presentations from renowned experts and a group discussion on problem-solving with large language models, complementing the innovative research papers that will serve as a basis for discussion.

Call for Papers and Submission

Paper submission link: https://wi-lab.com/cyberchair/2024/bigdata24/scripts/submit.php?subarea=S42&undisplay_detail=1&wh=/cyberchair/2024/bigdata24/scripts/ws_submit.php

Papers should be submitted single blind.

See below for updates about paper formats and templates.

The topics of interest are, but not limited to:

• Model Training and Optimization:

  • Techniques to deal with hallucinations

  • Training data for LLMs

  • Efficient and stable techniques for training and finetuning LLMs

  • Scalable approaches for distributed model training

  • Middleware for scale out data preparation for LLM training

  • Workflow orchestration for end-to-end LLM life cycle

  • Resource management for compute and energy efficient model training

  • Representation learning

• Model Utilization and Integration:

  • Using LLMs effectively as tools for Reinforcement Learning or search

  • Enhancing LLM capabilities by using external tools such as search engines

  • Visual Prompt Tuning and in-context learning

  • Enable easy experimentation with high utilization to train foundational models in the cloud

  • Strategies to scale resources for training/fine-tuning foundational models

  • Instruction tuning including generation of instruction tuning data

  • Parallel training: data model tensor (attention and weights)

  • Distributed workflows for data cleansing and model usage (Langchain)

  • Principled AI

  • Investigating reasoning capabilities of LLMs

  • Retrieval Augmented Generation

  • Alternative architectures such as State Space Models

• Compact Language Models and Knowledge Distillation:

  • Knowledge representations for training small/compact language models

  • Evaluation of different teacher-student distillation and model compression strategies

  • Techniques for efficient data encoding to maintain linguistic properties in compact models

  • Deployment of lightweight models in resource-constrained environments

  • Case studies on the effectiveness in various NLP tasks

• Application-Specific Models:

  • Math LLMs

  • Multimodal Foundation Models

  • Trustworthy Foundation Models

  • Large-scale Visual Foundation Models

  • Timeseries foundation models for forecasting, prediction and control

  • Multi-Agent System using LLMs

  • Recommender systems using LLMs

  • Knowledge management using LLMs

• Knowledge Incorporation and Adaptation:

  • Approaches to deal with knowledge recency to effectively update knowledge within LLMs

  • Incorporating domain knowledge in LLMs

• Evaluation and Benchmarking:

  • Additional benchmarks to fill gap between human and automatic reference-based evaluation

Paper formats are:

• Long papers:  up to 10 pages including all figures, tables, and references

• Short and vision papers: up to 6 pages including all figures, tables, and references

• Papers should be submitted single blind.

All papers must be submitted in the IEEE conference format:

• Official templates: https://www.ieee.org/conferences/publishing/templates.html

• Overleaf templates: https://www.overleaf.com/latex/templates/ieee-conference-template/grfzhhncsfqn

For Tutorials: Researchers and practitioners are invited to submit suggestions for tutorials. Please contact us via Rafet.Sifa@iais.fraunhofer.de with the title WLLFM Tutorials

Important Dates

• Dec 16, 2024, Workshop date

• Oct 31, 2024: Due date for full workshop papers submission

• Nov 15, 2024: Notification of paper acceptance to authors

• Nov 22, 2024: Camera-ready of accepted papers

• Dec 15-18 2024: Workshops (the exact workshop date will be determined based on the availability of the keynote speakers)

Organization

Workshop Chairs 

• Rafet Sifa (Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn and Fraunhofer IAIS Germany) 

• Dhaval Patel (IBM Research US)

• Linsey Pang (Salesforce US) 

• Wei Liu (University of Technology Sydney Australia)

Publication Chair

• Tobias Deußer (Fraunhofer IAIS Germany)

Program Committee

• Lucie Flek, Lamarr Institute For Artificial Intelligence and Machine Learning, Germany

• Christian Bauckhage, Lamarr Institute For Artificial Intelligence and Machine Learning, Germany

• Özlem Uzuner, George Mason University, USA

• Armin Berger, University of Bonn, Germany

• Aashish Jain, Salesforce, USA

• Zian Wang, Stony Brook university, USA 

• Qiushui Xu , Penn State University, USA 

• Qikai Yang, UIUC, USA

• Zheng Liu, Northeastern University, USA

• Tingting Tang, University of Southern California, USA

• Bo Yuan, Georgia Institute of Technology , USA 

• Yunzhe Wang, University of Southern California, USA

• Yong Liu, Salesforce, USA 

• Mounika Kamsali Veera, Walmart, USA 

• Lisa Pucknat, AXA, Germany

• Pengfei Li, Visa Research, USA

• Surya Lakshmi Sujitha Pasumarty, Albertsons, USA

• Maren Pielka, Fraunhofer IAIS, Germany

• Lorenz Sparrenberg, University of Bonn, Germany

• Yingfan Wang, Duke University, USA

• Tobias Uelwer, Fraunhofer IAIS, Germany

• Tian Long Xu, Squirrel AI Learning, USA

• Hao Yan, George Mason University, USA

• Mingxuan Yang, Brown University, USA

• Dezhi Yu, UC Berkeley, USA

• Haodong Zhang, NYU, USA

• Chenyang Zhao, UCLA, USA

Contact

For questions and comments please contact Rafet Sifa at: Rafet.Sifa@iais.fraunhofer.de