Special Session

Special Session Ⅰ: Language Intelligence and Data Storage Revolution: The Role of Natural Language Processing in Solid-State Storage/Embedded Systems

Session Chair: Assoc. Prof. Jinhua Cui & Assoc. Prof. Meng Zhang——Huazhong University of Science and Technology, China

Session Co-Chair: Asst. Prof. Shiqiang Nie——Xi'an Jiaotong University, China

Special Session Information:

With the rapid advancement of information technology, we find ourselves amidst the tidal wave of the information age. Solid-state storage systems and embedded technologies have become integral components of modern computing systems, while the advancements in natural language processing (NLP) continue to shape the way we handle data. This special session aims to explore how NLP plays a pivotal role in core technologies of this digital world and its applications and challenges within solid-state storage systems and embedded systems.

Below is an incomplete list of potential topics to be covered in the Special Session:

Topics of interest include but are not limited to:

• Data management and optimization strategies driven by language intelligence

• The role of NLP in enhancing the performance of solid-state storage systems

• The applications of speech recognition and natural language interaction in embedded systems

• Intelligent recommendation and control technologies based on NLP in embedded systems

• Data mining and analysis in embedded systems powered by NLP techniques

• Compression, encoding, and storage optimization of textual data

• Data security and privacy protection techniques based on NLP

• Applications and challenges of NLP in ensuring security in embedded systems

Important Dates:

Abstract Submission: May 24, 2025

Full Paper Submission: May 31, 2025

Special Session Ⅱ: Responsible LLMs for Reasoning

Session Chair: Assoc. Prof. Kun Zhang——Hefei University of Technology, China

Assoc. Prof. Kai Zhang——University of Science and Technology of China, China

Key Words: responsible large language models; large model reasoning; knowledge reasoning; causal reasoning

Special Session Information:

In the era of large language models represented by GPT and ChatGPT, responsible generative artificial intelligence technologies are crucial because they can greatly affect the quality of content generated by large language models. AI algorithms such as ChatGPT, GPT, and BARD are designed to learn from large amounts of data and make inferential responses based on semantic correlations found in the data. While this could lead to significant advances in areas such as healthcare, transportation and finance, it could also lead to unintended consequences if developed irresponsibly. In the case of ChatGPT, responsible reasoning with LLMs means ensuring that large language models are not used to spread disinformation, perpetuate harmful stereotypes, or engage in unethical behavior. This field has huge potential to bring positive economic and social impact and build a responsible and sustainable AI future.

This special topic aims to attract manuscripts closely related to responsible LLMs. Potential topics of interest include but are not limited to:

• Causal-enhanced LLMs inference method

• Robust LLMs inference method

• Bias and fairness of LLMs inference

• Ethical considerations in the development of LLMs

• Fairness, Accountability and Transparency in LLMs

• Bias Mitigation and Equity Awareness LLMs

• Explainable AI and explainable LLMs

• Robust and resilient LLMs against adversarial attacks

• Human-centered design of LLMs

• Social and cultural impacts of LLMs development

• Ethics, Standards and Regulations for Responsible LLMs

Important Dates:

Submission Deadline: May 30, 2025

Special Session Ⅲ: Multimodal Large Language Model and Its Application in the Field of Transportation

Session Chair: Assoc. Prof. Wenjuan Han——Beijing Jiaotong University, China

Key Words: Multimodality, Large Language Model, Transportation, Application, Technology

Special Session Information:

The transportation field is undergoing a significant revolution driven by artificial intelligence (AI). Multimodal Large Language Models (MLLMs), a novel class of AI models, are poised to play a pivotal role in this transformation. These models can process and understand information from various sources, including text, images, and videos, making them uniquely suited to address the complex challenges of modern transportation systems.

This session will delve into the exciting world of MLLMs and explore their diverse technologies and applications within the transportation field. We will:

* Demystify MLLMs: Gain a foundational understanding of MLLMs, grasp their capabilities, and new technologies.

* Explore Real-World Applications: Discover how MLLMs are being used to enhance various aspects of transportation, from intelligent traffic management and personalized route optimization to advanced passenger services and automated vehicle operations.

* Find Challenges and Opportunities: Discuss the key challenges associated with the implementation of MLLMs in transportation, including ethical considerations, data privacy, and infrastructure integration. We will also explore the vast opportunities these models present for building a more efficient, sustainable, and user-centric transportation system.

This session is designed for a wide audience, including transportation professionals, researchers, policymakers, students, and anyone interested in the future of mobility. Whether you are a seasoned expert or just embarking on your journey into this exciting field, this session will provide valuable insights and foster discussions about how MLLMs can shape the future of transportation.

Topics of interest include but are not limited to:

• Technical Deep Dives

• Applications of MLLMs

• Safety and Security of MLLMs in Transportation

• Ethical Implications of MLLMs

• Future of Work in Transportation

• Public Perception and Adoption

Important Dates:

Submission Deadline: June 25, 2025