Call For Papers

The International Conference on Large Language Models (LLM-2026) is a premier global event that brings together researchers, academicians, industry professionals, and policymakers to exchange knowledge, share pioneering research, and explore the transformative capabilities of Large Language Models (LLMs) in shaping the future of artificial intelligence, communication, and knowledge systems.

LLM-2026 aims to advance interdisciplinary understanding by bridging foundational LLM research with real-world applications across science, technology, society, and industry. The conference provides a collaborative platform to discuss breakthroughs in model architectures, training methodologies, ethical frameworks, and deployment strategies, fostering a deeper understanding of how LLMs are redefining human–machine interaction, knowledge creation, and automated reasoning.

Participants will gain insights into cutting-edge developments in multimodal learning, generative AI, cognitive modeling, and responsible AI governance, while exploring how LLMs can empower innovation, enhance productivity, and support sustainable digital transformation across multiple sectors. LLM-2026 welcomes original research papers, case studies, and conceptual contributions from academia, research organizations, and industry. Whether your focus lies in improving model efficiency, addressing ethical concerns, developing domain-specific LLMs, or leveraging LLMs in business, science, or education, this conference is designed to inspire discourse, foster collaboration, and chart the roadmap for the next generation of intelligent systems.

LLM-2026 invites original work from the following list of topics (but is not limited to):

1. Foundations and Architectures of Large Language Models

• Transformer and post-transformer architectures
• Pre-training, fine-tuning, and alignment techniques
• Retrieval-augmented generation (RAG) and hybrid models
• Model compression, quantization, and scalability optimization

2. Training Data, Knowledge Representation, and Evaluation

• Data curation, filtering, and synthetic data generation
• Knowledge encoding and retrieval strategies
• Evaluation metrics and benchmarking frameworks
• Continual learning and adaptive reasoning in LLMs

3. Multimodal and Multilingual Models

• Vision–language and speech–language integration
• Multilingual and cross-lingual transfer learning
• Multimodal reasoning and grounded language understanding
• Low-resource and indigenous language modeling

4. Cognitive, Linguistic, and Human–AI Interaction Studies

• Cognitive modeling and reasoning capabilities of LLMs
• Pragmatic understanding, coherence, and discourse generation
• Emotion, empathy, and creativity in generative AI
• Human–AI collaboration, alignment, and feedback learning

5. Ethics, Fairness, and Responsible AI

• Bias detection and mitigation in language models
• Transparency, explainability, and interpretability
• Privacy-preserving and sustainable AI practices
• Ethical and legal governance of generative systems

6. Security, Robustness, and Trustworthiness

• Adversarial prompt injection and jailbreak defense
• Toxicity detection and hallucination control
• Model watermarking and content verification
• Safe deployment frameworks and risk management

7. Applications of LLMs in Science, Engineering, and Education

• Research automation and scientific discovery using LLMs
• Knowledge synthesis and data-driven exploration
• Intelligent tutoring and adaptive learning environments
• AI copilots for coding, design, and innovation

8. LLMs in Business, Economics, and Decision Intelligence

• Financial forecasting and market sentiment analysis
• LLMs for strategic planning and process automation
• Policy simulation and data-driven governance
• Digital transformation and organizational intelligence

9. Emerging Trends and Future Directions

• Federated and decentralized LLMs
• Quantum and neuromorphic architectures
• Open-source ecosystems and democratized AI research
• Evaluating general intelligence and reasoning capacity