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2026 AITIA 大型语言模型与生成式人工智能研讨会 (AITIA-LLMGAI 2026)
发布时间：2026/09/04
309 会议地点：Hong Kong, China
会议时间：December 28, 2026
截稿时间：November 20, 2026
会议简介
The 2026 AITIA Symposium on Large Language Models and Generative Artificial Intelligence (AITIA-LLMGAI 2026) will be held on December 28, 2026 in Hong Kong, China.
As a focused symposium of the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026), the event provides an interdisciplinary platform for researchers, engineers, developers and industry professionals to exchange recent advances in large language models and generative artificial intelligence.
The symposium focuses on the foundations, architectures, training methods, evaluation, applications, governance and responsible development of generative AI systems. Particular attention is given to research connecting language models with multimodal intelligence, intelligent agents, knowledge systems, scientific discovery and real-world applications.
Original research papers, review articles, methodological studies, system designs, engineering applications and interdisciplinary case studies are welcome. The symposium aims to support academic exchange and responsible innovation across artificial intelligence, computer science and related fields.
论文收录
Accepted and registered papers will be published in the official symposium proceedings entitled Proceedings of the 2026 AITIA Symposium on Large Language Models and Generative Artificial Intelligence, abbreviated as Proceedings of AITIA-LLMGAI 2026.
Bibliographic metadata for published papers will be deposited with Crossref. Eligible papers will also be submitted to Google Scholar, CNKI, Conference Proceedings Citation Index (CPCI) and other relevant academic databases for evaluation and possible indexing.
Papers that have completed peer review, registration, final manuscript checks and production requirements may be transferred to publication on a regular basis. Papers registered earlier may enter the production process earlier.
Submission to an academic database does not guarantee inclusion or indexing. Final indexing results are determined independently by each database and may be affected by its evaluation criteria, policies, workflow and processing schedule.
征稿范围
1. Large Language Model Foundations
Transformer architectures and variants
Foundation models and language representation
Pre-training objectives and strategies
Scaling laws and model efficiency
Tokenization and vocabulary design
Long-context language modelling
Multilingual and cross-lingual models
Domain-specific language models
Knowledge representation in language models
Theoretical analysis of generative models
2. Training, Adaptation and Optimization
Instruction tuning and supervised fine-tuning
Reinforcement learning from human feedback
Parameter-efficient fine-tuning
Prompt tuning and prompt optimization
Model compression and knowledge distillation
Quantization and efficient inference
Distributed and federated model training
Synthetic data generation and augmentation
Continual and lifelong learning
Hardware-aware model optimization
3. Multimodal Generative Intelligence
Vision-language foundation models
Text-to-image generation
Text-to-video generation
Speech and audio generation
Cross-modal representation learning
Multimodal reasoning and understanding
Image and video captioning
Generative 3D content and virtual environments
Multimodal retrieval and generation
Unified multimodal foundation models
4. Intelligent Agents and Applications
LLM-based autonomous agents
Retrieval-augmented generation
Tool use and function calling
Multi-agent systems and collaboration
Conversational AI and dialogue systems
Code generation and software engineering
Generative AI for education
Generative AI for healthcare
Generative AI for scientific discovery
Enterprise and industrial AI applications
5. Evaluation, Reliability and Security
Language-model evaluation benchmarks
Factuality and hallucination detection
Reasoning and planning evaluation
Robustness and adversarial testing
Model uncertainty and calibration
Explainability and interpretability
Privacy-preserving generative AI
Prompt injection and model security
Content authenticity and provenance
Human evaluation of generative systems
6. Ethics, Governance and Social Impact
Responsible generative AI
Fairness, bias and discrimination
AI alignment and human values
Copyright and intellectual property
Data governance and informed consent
AI-generated misinformation
Transparency and model documentation
Legal and regulatory frameworks
Environmental impact of foundation models
Social and economic impacts of generative AI
会议时间：December 28, 2026
截稿时间：November 20, 2026
会议简介
The 2026 AITIA Symposium on Large Language Models and Generative Artificial Intelligence (AITIA-LLMGAI 2026) will be held on December 28, 2026 in Hong Kong, China.
As a focused symposium of the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026), the event provides an interdisciplinary platform for researchers, engineers, developers and industry professionals to exchange recent advances in large language models and generative artificial intelligence.
The symposium focuses on the foundations, architectures, training methods, evaluation, applications, governance and responsible development of generative AI systems. Particular attention is given to research connecting language models with multimodal intelligence, intelligent agents, knowledge systems, scientific discovery and real-world applications.
Original research papers, review articles, methodological studies, system designs, engineering applications and interdisciplinary case studies are welcome. The symposium aims to support academic exchange and responsible innovation across artificial intelligence, computer science and related fields.
论文收录
Accepted and registered papers will be published in the official symposium proceedings entitled Proceedings of the 2026 AITIA Symposium on Large Language Models and Generative Artificial Intelligence, abbreviated as Proceedings of AITIA-LLMGAI 2026.
Bibliographic metadata for published papers will be deposited with Crossref. Eligible papers will also be submitted to Google Scholar, CNKI, Conference Proceedings Citation Index (CPCI) and other relevant academic databases for evaluation and possible indexing.
Papers that have completed peer review, registration, final manuscript checks and production requirements may be transferred to publication on a regular basis. Papers registered earlier may enter the production process earlier.
Submission to an academic database does not guarantee inclusion or indexing. Final indexing results are determined independently by each database and may be affected by its evaluation criteria, policies, workflow and processing schedule.
征稿范围
1. Large Language Model Foundations
Transformer architectures and variants
Foundation models and language representation
Pre-training objectives and strategies
Scaling laws and model efficiency
Tokenization and vocabulary design
Long-context language modelling
Multilingual and cross-lingual models
Domain-specific language models
Knowledge representation in language models
Theoretical analysis of generative models
2. Training, Adaptation and Optimization
Instruction tuning and supervised fine-tuning
Reinforcement learning from human feedback
Parameter-efficient fine-tuning
Prompt tuning and prompt optimization
Model compression and knowledge distillation
Quantization and efficient inference
Distributed and federated model training
Synthetic data generation and augmentation
Continual and lifelong learning
Hardware-aware model optimization
3. Multimodal Generative Intelligence
Vision-language foundation models
Text-to-image generation
Text-to-video generation
Speech and audio generation
Cross-modal representation learning
Multimodal reasoning and understanding
Image and video captioning
Generative 3D content and virtual environments
Multimodal retrieval and generation
Unified multimodal foundation models
4. Intelligent Agents and Applications
LLM-based autonomous agents
Retrieval-augmented generation
Tool use and function calling
Multi-agent systems and collaboration
Conversational AI and dialogue systems
Code generation and software engineering
Generative AI for education
Generative AI for healthcare
Generative AI for scientific discovery
Enterprise and industrial AI applications
5. Evaluation, Reliability and Security
Language-model evaluation benchmarks
Factuality and hallucination detection
Reasoning and planning evaluation
Robustness and adversarial testing
Model uncertainty and calibration
Explainability and interpretability
Privacy-preserving generative AI
Prompt injection and model security
Content authenticity and provenance
Human evaluation of generative systems
6. Ethics, Governance and Social Impact
Responsible generative AI
Fairness, bias and discrimination
AI alignment and human values
Copyright and intellectual property
Data governance and informed consent
AI-generated misinformation
Transparency and model documentation
Legal and regulatory frameworks
Environmental impact of foundation models
Social and economic impacts of generative AI
联系方式：
会议官网：https://www.aitia-conf.org/llmgai
投稿链接：https://www.aitia-conf.org/submission
投稿链接：https://www.aitia-conf.org/submission
来源/发布：https://www.aitia-conf.org/llmgai
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