SciProdLLM 2025 is a forum for presenting and discussing research on integrating large language models (LLMs) into the typical research workflow: from ideation to experimentation to scientific writing, with a particular focus on human-centered approaches that ensure ethical and responsible use of LLMs. We also invite work that evaluates the quality of LLM-assisted research workflows and the resulting outputs.

We welcome submissions on any aspect of human-LLM collaboration for science production, evaluation of LLMs for science production, and/or evaluation of LLM-assisted scientific papers. Relevant topics include, but are not limited to, the following:

- Guiding idea generation through user feedback
- Automated experimentation following the experimental workflow used by human scientists (e.g., the workflow from data preprocessing to comparison to baselines)
- Human-curated datasets of scientific papers for fine-tuning LLMs for generating ideas and paper content (text, figures, tables, etc.)
- Human-LLM co-authored peer reviews (e.g., LLM-assisted peer review platforms)
- Benchmark datasets for evaluating LLMs on idea generation, experimentation, multimodal content generation, or scientific writing
- Evaluation metrics for detecting problematic papers (e.g., those containing suspicious citations or tortured phrases)
- Statistical analyses of collections of LLM-assisted papers (e.g., on topics, citations, or retractions)


SUBMISSION INSTRUCTIONS

SciProdLLM 2025 welcomes long and short papers. Long papers may consist of up to 8 pages of content, plus unlimited pages of references. Short papers may consist of up to 4 pages of content, plus unlimited pages of references. Both types of submissions must follow the same requirements and procedures as for IJCNLP-AACL 2025 main conference papers: (https://2025.aaclnet.org/calls/main_conference_papers) Note that papers submitted as non-archival will be allocated presentation time at the workshop but will not be included in the proceedings.

There are three supported submission modes:

- Direct submissions: Direct submissions will receive up to three double-blind reviews, and a final decision on acceptance from the workshop organizers. Direct submissions should be made through the SciProdLLM page on OpenReview: (https://openreview.net/group?id=aclweb.org/AACL-IJCNLP/2025/Workshop/SciProdLLM)

- ARR submissions: Unpublished papers that have already been reviewed and meta-reviewed through ACL Rolling Review may be committed to SciProdLLM. These papers will not receive new reviews but may be meta-reviewed by the workshop organizers, who will make a final decision on acceptance. A commitment link will published in a future version of this call.

- Previously published papers: We invite non-archival submissions of papers that have already been recently published elsewhere. This allows such papers to gain more visibility from the workshop audience. To submit a previously published paper for presentation, please email SciProdLLM@groups.io with the details of your paper (title, authors, abstract, publication venue) and attaching a PDF copy of the paper. (Submissions of previously published papers need not adhere to the IJCNLP-AACL 2025 main conference paper policies on anonymity.)


IMPORTANT DATES

All deadlines are 23:59 UTC−12 ("Anywhere on Earth").

- September 29, 2025: Submission deadline for direct submissions
- October 27, 2025: ARR commitment deadline
- November 3, 2025: Notification of acceptance
- November 11, 2025: Camera-ready papers due
- December 23 or 24, 2025: Workshop presentations (exact date TBA)


ORGANIZING COMMITTEE

- Wei Zhao, University of Aberdeen, UK
- Jennifer D’Souza, TIB Leibniz Information Center for Science and Technology, Germany
- Steffen Eger, University of Technology Nuremberg, Germany
- Anne Lauscher, University of Hamburg, Germany
- Yufang Hou, IT:U Interdisciplinary Transformation University, Austria
- Nafise Sadat Moosavi, University of Sheffield, UK
- Tristan Miller, University of Manitoba, Canada
- Chenghua Lin, University of Manchester, UK


CONTACT INFORMATION

Email: SciProdLLM@groups.io
WWW: https://sciprodllm.github.io/