- Submission Deadline: 1 September 2023
- First Notification: 1 November 2023
- Revisions Due: 1 January 2024
- Final Notification: 1 March 2024
Publication: Mid 2024
Pre-Trained Large Language Models (LLMs), such as ChatGPT and GPT-4, have revolutionized the field of AI with their remarkable capabilities in natural language understanding and generation. LLMs are widely used in various applications such as voice assistants, recommender systems, content-generation models like ChatGPT and text-to-image models like Dall-E. However, these powerful models also pose significant challenges for their safe and ethical deployment. How can we ensure that LLMs are fair, safe, privacy-preserving, explainable, and controllable?
The special issue will cover two main themes:
- ) to present recent progress of foundational LLMs and their applications in different domains;
- ) to address open issues and challenges for building trustworthy LLMs. We hope that this special issue will foster interdisciplinary collaboration and contribute to the development and use of LLMs that benefit humanity.
We welcome submissions on recent advances and applications of large language models (LLMs) together with an emphasis on enhancing trust in the use of LLMs.
- Advanced model architectures for LLMs, e.g., Trans-former architectures and attention mechanisms.
- Advanced algorithms for improving performance, cost, robustness, and complexity of LLMs.
- Model transfer and compression techniques for LLMs.
- Federated Learning for LLMs.
- Prompt Engineering for LLMs.
- Innovative applications of LLMs in various domains, e.g., psychotherapy, elderly care, etc.
- Educational technologies based on LLMs such as chat-bots, content generation, feedback systems, etc.
- Natural language understanding and generation tasks using LLMs, e.g., storytelling, marketing copywriting, etc.
- LLMs for health care, protein synthesis, etc.
- Ethics, social economics, and trustworthiness of LLMs.
- Data labeling and quality issues for training LLMs.
- Privacy and security risks of models and data used by LLMs.
- Potential bias and unfairness in the output of LLMs
- Human oversight and intervention mechanisms for con-trolling LLMs.
- Hallucination detection and alleviation for LLMs.
- Emergent behavior in LLMs
For author information and guidelines on submission criteria, please visit the TBD’s Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Questions? Contact the guest editors:
- Yuxiao Dong, Tsinghua University
- Qiang Yang, Hong Kong University of Science and Technology & WeBank AI
- Chang Zhou, Alibaba Group
- Xuezhi Wang, google brain
- Qiaozhumei, Michigan university