Large Language Models and the future of scientific publishing
11.02.2025 , 10:00 – 16:00 CET
Join our workshop to explore the chances and challenges Large Language Models pose for scientific writing and academic journals.
Interact with researchers, publishers, and LLM tools to navigate the evolving landscape together!
Large Language Models (LLMs) have already started to significantly impact scientific workflows. Their influence is set to grow even further and at speeds that exceed previous transformational changes. LLMs will most discernably affect the most central element of science communication: publications. They will decrease the burden of creating and editing scientific articles, which are the culmination of research data production. Tools are already under development (e.g. Paperpal) that decrease the duration of creating the first draft. LLMs therefore have potential to significant reduce the time from data collection to publication. These software developments will help reap the benefits of research data in a timely fashion but will also ultimately result in an increase of journal submissions. Existing quality assurance mechanisms (especially peer review) will therefore be challenged both in terms of volume but also in term of the criteria to assess the value-added of publications.
This workshop will bring together researchers and publishers to identify and address the upcoming challenges for the journals as well as the increased demands on the reviewers and the wider research community. Furthermore, the workshop will identify possible chances enabled by incorporating LLM technologies within the writing and publishing process and open the path for future discussions on this topic. Thereby, we accelerate the exchange and harmonization of user journeys for the future of scholarly publication.
“Since 2023, it became much more difficult to find work as an academic copy editor compared with the previous 10 years. This coincides with the widespread use of ChatGPT. As a result, I had to find a new occupation.” SK (North American academic copy editor)
