In the first LLM@IJCAI'23 Symposium call for papers, excellent submitted papers are recommended to be published in "AI Open" (EI search) and "JCST" (CCF-B).
Large-scale language models (LLMs), such as ChatGPT and GPT-4, have revolutionized the field of artificial intelligence 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 (such as ChatGPT), and text-to-image models (such as Dall-E), etc.
However, these powerful models also pose significant challenges to their safe and ethical deployment. How can we ensure that LLMs are fair, safe, privacy-preserving, explainable, and controllable?
To provide a platform for academic researchers and industry practitioners to discuss the latest progress and open problems in the field of LLMs. Professor Chen Lei, Professor Yang Qiang from Hong Kong University of Science and Technology, and Professor Tang Jie from Tsinghua University organized the first Symposium on Large Language Models (LLM 2023) at the IJCAI2023 conference, a seminar on the progress and problems of large models.
The workshop will cover two topics: a) introduce the latest advances in fundamental LLMs and their applications in different fields; b) address the issues and challenges faced in building trustworthy LLMs.
It is hoped that this seminar will promote interdisciplinary cooperation and exchange of ideas among the participants, and contribute to the development and use of LLMs that can benefit mankind, and excellent submitted papers will be recommended to "AI Open" (EI search) and "JCST" (CCF-B) published.
Official website address: https://bigmodel.ai/llm-ijcai23/
Keynotes
Call for Papers
LLM@IJCAI'23 is a non-archived symposium, and each accepted paper must have at least one author present at the conference to present the paper.
We welcome contributions on recent advances and applications of large models (LLMs), with an emphasis on increasing trust in the use of LLMs, and topics for discussion include (but are not limited to):
Techniques:
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Advanced model architectures for LLMs, e.g., Transformer architectures and attention mechanisms.
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Advanced algorithms for improving performance, cost, robustness, and complexity of LLMs.
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Model transfer and compression techniques for LLMs.
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Federated Learning for LLMs.
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Prompt Engineering for LLMs.
Applications:
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Innovative applications of LLMs in various domains, e.g., psychotherapy, elderly care, etc.
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Educational technologies based on LLMs such as chat-bots, content generation, feedback systems, etc.
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Natural language understanding and generation tasks using LLMs, e.g., storytelling, marketing copywriting, etc.
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LLMs for health care, protein synthesis, etc.
Challenges
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Ethics, social economics, and trustworthiness of LLMs.
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Data labeling and quality issues for training LLMs.
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Privacy and security risks of models and data used by LLMs.
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Potential bias and unfairness in the output of LLMs
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Human oversight and intervention mechanisms for controlling LLMs.
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Hallucination detection and alleviation for LLMs.
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Emergent behavior in LLMs
important date
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Paper submission deadline: June 10, 2023
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Date of paper acceptance notification: June 24, 2023
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Date of seminar: August 21, 2023
submission method
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Each submission has a maximum of 7 pages of content, plus a maximum of 2 pages of references and acknowledgments.
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Submitted papers must be written in English and saved as PDF files in accordance with the template format of IJCAI'23.
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All submitted papers will undergo a one-way blind review to evaluate their performance in terms of innovation, technical quality and impact.
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Submitted papers can contain author details.
Please submit through the Easychair submission website:
https://easychair.org/conferences/?conf=llmijcai23
According to the requirements of IJCAI'23, at least one author of each selected paper must attend the IJCAI meeting in person. Also, the same paper cannot be submitted to multiple IJCAI seminars/seminars at the same time.
organizer
General Co-Chairs
Chen Lei, Hong Kong University of Science and Technology (Guangzhou)
Tang Jie, Tsinghua University
Yang Qiang, Hong Kong University of Science and Technology, WeBank AI
Program Co-Chairs
Dong Yuxiao, Tsinghua University
Fan Lixin, WeBank AI
If you have any questions, please email: [email protected].