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PromptEng 2024 : The International Workshop on Prompt Engineering Large Language Models


When Nov 26, 2024 - Nov 29, 2024
Where Dubai, UAE
Submission Deadline Aug 15, 2024
Notification Due Oct 5, 2024
Final Version Due Oct 20, 2024
Categories    generative ai   fine tuning   large language models   prompt engineering

Call For Papers

Prompt engineering stands at the forefront of harnessing the potential of foundation models (FMs) and Large Language Models (LLMs). In the rapidly evolving landscape of AI, understanding the intricacies of prompt design is paramount for unleashing the full capabilities of models like BERT, T5, ChatGPT, and others. This workshop aims to delve deep into the realm of prompt engineering, exploring topics ranging from human-centric design principles to ethical considerations. By fostering collaboration and discussion, we seek to address challenges such as bias mitigation, transparency, and accountability while charting a course towards ethical AI development. Join us as we navigate the complexities of prompt engineering and shape the future of language models.

Authors are invited to submit their original work, which is not submitted elsewhere, to this workshop. The accepted papers of the workshop will be published by the IEEE Conference Publishing Services (CPS) and will be submitted for inclusion in the IEEE-Xplore and the IEEE Computer Society (CSDL) digital libraries. We invite the submission of original papers on all related topics related to PromptEng, with special interest in but not limited to:

Prompt Engineering Techniques
Human-Centric Design Principles in Prompt Engineering for LLMs
Strategies and Bias Mitigation in Prompt Design for Diverse Applications
Transparency, Accountability, and Ethical Considerations in LLMs Development
Trade-offs in Prompt Design: Balancing Utility and Performance
Collaborative and Continuous Improvement Strategies in Prompt Engineering
Ethical Prompt Engineering: Cross-Cultural and Multilingual Challenges and Solutions
Fostering User Trust and Ethical AI through Effective Prompt Engineering Practices
Fine tuning

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