2026 AITIA 大型语言模型与生成式人工智能研讨会 (AITIA-LLMGAI 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