Call for Papers

Given the surge in the technological advancements in deep learning, machine learning, and advanced large-scale models that are trained on large amount of data at scale and adaptable to a wide range of real applications, AI is experiencing a paradigm revolution, driving the evolution of complex and advance machine intelligence (AMI). Among them, Large Language Models (LLMs) such as BERT, T5, ChatGPT, GPT-4, Falcon 180B, Codex, DALL-E, Whisper, and CLIP are now the foundation for new applications ranging from computer vision to protein sequence study and from speech recognition to coding. Nowadays, mostly large tech companies such as OpenAI, Google, Facebook, and Amazon can afford to construct and leverage the power of LLMs. Despite the expected widely publicized use of LLMs, we still lack a comprehensive knowledge of how they operate, planning, and reasoning, why they underperform, and what they are even capable of because of their emerging global qualities. To deal with these problems, we believe that much critical research on LLMs for planning and reasoning would necessitate extensive multidisciplinary collaboration, given their essentially social and technical structure.

The First IEEE International Symposium on Large Language Models for Planning and Reasoning (IEEE LLM-PR 2024) addresses the architectures, applications, challenges, approaches, and future directions of LLMs and applications. We invite the submission of original papers on all topics related to LLMs for planning and reasoning, with special interest in but not limited to:

Science of LLMs: scaling laws, fundamental limitations, emergent capabilities, demystification, interpretability, transparency, complexity, training dynamics, and learning theory for LLMs.
Learning Algorithms for LLMs: learning, meta learning, model mixing methods, continuous learning, and hieratically learning for LLMs.
Inference Algorithms for LLMs: encoding algorithms, planning algorithms, search algorithms, reasoning algorithms, and sequential decision making for LLMs.
Multimodal Models of LLMs: Integration of text with other modalities like images, video, and audio; multimodal representation learning; applications of multimodal models for LLMs.
LLMs Interfaces and Interaction: design and implementation of Natural Language Interfaces, user studies with human participants on Conversational User Interfaces, chatbots and LLM-based chatbots and their interaction with users.
Smart Question Answering (QA): Natural language interfaces to databases, QA using web data, multi-lingual QA, non-factoid QA (how/why/opinion questions, lists), geographical QA, QA corpora and training sets, QA over linked data (QALD).
Corpus Analysis: multi-lingual, multi-cultural and multi-modal corpora; machine translation, text analysis, text classification and clustering; language identification; plagiarism detection; information extraction: named entity, extraction of events, terms, and semantic relationships.
LLMs in Conceptual Modelling: analysis of natural language descriptions, LLMs in requirement engineering, terminological ontologies, consistency checking, metadata creation and harvesting.

Submission

Submissions should include abstract, 5-10 keywords, and the e-mail address of the corresponding author and be in PDF format. Each submission should be in the IEEE 8.5 x 11 inches two-column format with 10-12 point font (Template: DOC or LaTeX), including tables, figures, and references.

Each accepted paper needs at least one author to register in the conference and give an oral presentation at the conference. This process is mandatory for all authors. We assume authors understand this policy before they submit the paper to this conference. For any reasons the authors cannot attend the conference, that will be the authors’ responsibility to take consequences. Any paper withdrawals may result in a punishment given by IEEE SmartCloud.

Each submission should be regarded as an undertaking that, should the submission be accepted, at least one of the authors must attend the conference to present the work.

Considering some authors prefer publishing longer papers, we allow authors to submit up to 12 pages (6 complimentary pages and up to 6 extra pages) for regular papers, up to 5 pages (3 complimentary pages and up to 2 extra pages) for short papers/industrial papers, we do not encourage authors to submit overlength papers due to the over length charge. Refer to the following instructions for detailed information contact the over length charge:

PAPER PAGE LIMIT:

Regular Papers: 6 complimentary pages and up to 6 extra pages, including all figures, tables, and references. (Authors can have 6 complimentary pages. Manuscript can be up to 12 pages with over length charge $150/page)

Short Papers:3 complimentary pages and up to 2 extra pages, including all figures, tables, and references. (Authors can have 3 complimentary pages. Manuscript can be up to 5 pages with over length charge $150/page) Poster: 1 complimentary page and up to 1 extra page, including all figures, tables, and references. (Authors can have 1 complimentary page. Manuscript can be up to 2 pages with over length charge $150/page) We also encourage the submission of visionary paper and survey paper, which is belong to type 1.

All paper submissions must represent original and unpublished work. When you submit a paper to LLM-PR 2024, please submit it to IEEE SmartCloud together. In the title, please use the style "LLM-PR: paper title".

Submissions link

Important Note: Counting the cost of the over length charge uses the longest submission version (e.g. submission version, updated version, camera-ready version). The purpose of this policy is to save reviewers’ time and our operation time.

Organization Commitees

General Chair
Zhong Chen, School of Computing, Southern Illinois University, United States

Program Commitee Chair
Yi He, Department of Computer Science, Old Dominion University, United States
Wenbin Zhang, School of Computing & Information Sciences, Florida International University, United States

Publicity Chair
Di Wu, College of Computer and Information Science, Southwest University, China

Technical Program Committee
Ka-Ho Chow, Department of Computer Science, University of Hong Kong, Hong Kong
Xin Zheng, Department of Computer Science, Monash University, Australia
Haolong Xiang, School of Computing, Macquarie University, Australia
Xinyi Wu, Institute for Data, Systems, and Society (IDSS), Massachusetts Institute of Technology (MIT), United States
Yao Su, Computer Science Department, Worcester Polytechnic Institute, United States
Yao Qiang, College of Engineering, Wayne State University, United States
Bonald Ziyue Li, Information System Department, University of Cologne, Germany

Contact

If you have any questions or queries on LLM-PR 2024, please contact us via an email.

Email address: cloud-conf@outlook.com

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