Fourth Workshop on Large Language and Foundation Models (WLLFM 2026)
Location: Sheraton Phoenix Downtown, Phoenix, AZ, USA
Conference: BigData 2026 (IEEE International Conference on Big Data)
Date: December 14th–17th, 2026
Large language models (LLMs) and foundation models (FMs) have rapidly emerged as pivotal technologies in data science and analytics, offering unprecedented capabilities in text generation, knowledge extraction, and complex decision-making. However, a significant gap remains between the rapid theoretical advancements in these models and their robust, scalable deployment in industrial environments.
This workshop seeks to bridge cutting-edge theory with real-world applications, providing a venue for researchers and practitioners to exchange novel methodologies, deployment strategies, and impact-driven insights. By spotlighting both breakthrough techniques and operational challenges (such as scalability, interpretability, and ethics), the session aims to foster cross-pollination of ideas and accelerate the seamless integration of large language models into diverse data-driven ecosystems.
- Submission Deadline: TBA (tentative: early October 2026)
- Paper Notification: TBA (tentative: early November 2026)
- Paper Camera-Ready: November 13th, 2025
- Contact:
amllab[at]bit.uni-bonn.de
Submission
Submission Link: https://wi-lab.com/cyberchair/2026/bigdata26/scripts/submit.php?subarea=S22&undisplay_detail=1&wh=/cyberchair/2026/bigdata26/scripts/ws_submit.php
Papers should be submitted single blind.
Paper formats are:
- Long papers: up to 10 pages including all figures, tables, and references
- Short and vision papers: up to 5 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
Call for Papers
The topics of interest are, but not limited to:
- State-of-the-Art Model Research:
- Model training, optimization, and architecture
- Architectures beyond the Transformer: state-space models, linear-attention variants, hybrid designs, and diffusion language models
- Reasoning models and test-time compute scaling
- Systems and Efficiency:
- Inference at scale: efficiency, latency, memory use, routing, and serving-time optimization for large models, including long-context and sparse architectures
- Efficient fine-tuning, adaptation, and post-training of LLMs or FMs: supervision design, synthetic data, reward signals, preference optimization, distillation, and curated training pipelines
- Industrial use cases: challenges in latency, cost-optimization, and hardware constraints
- Retrieval and Context Management:
- How models obtain, select, organize, and use external information through retrieval, reranking, and context construction
- Retrieval-Augmented Generation (RAG) in enterprise environments
- Agentic and Multi-Agent Systems:
- Multi-agent LLM systems and orchestration: role specialization, agent-to-agent protocols, and standards such as MCP for tool integration
- Autonomous software-engineering agents and LLM-driven code generation in production codebases
- Multimodal and Domain-Specific Foundation Models:
- Multimodal and any-to-any foundation models spanning vision, audio, and video
- Foundation models for structured big-data modalities, including time series, tabular, graph, and geospatial data
- Domain-specific foundation models for finance, healthcare, legal, and scientific discovery, with specialized constraints and metrics
- Quantum Foundation Models
- Privacy, Safety, and Ethics:
- Privacy-preserving and federated training or inference for LLMs and FMs
- Evaluation and trustworthiness: robustness, reliability, safety, long-context behavior, and “in-the-wild” usefulness beyond narrow benchmark scores
- Interpretability and explainability of foundation models in decision-critical systems
- Ethical, societal, and regulatory considerations in LLM adoption
Proceedings and Indexing
All accepted workshop papers will be published by IEEE in the BigData 2026 Proceedings and will be submitted for inclusion in the IEEEXplore Digital Library.
Organizers
- Prof. Dr. Rafet Sifa (University of Bonn / Fraunhofer IAIS, Germany)
- Dr. Tobias Deußer (University of Bonn, Germany)
- Prof. Dr. Aurelio Bariviera (University of Rovira i Virgili, Spain)
- Dr. Dhaval Patel (IBM Research, USA)
- Prof. Dr. Wei Liu (University of Technology Sydney, Australia)
- Dr. Lorenz Sparrenberg (University of Bonn, Germany)
- Dr. Linsey Pang (PayPal, USA)
- Dr. Thore Gerlach (European Space Agency, Netherlands)
This workshop has been partially funded by the Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr-Institute for Machine Learning and Artificial Intelligence.