Bridging the gap between scientists and large language models

Abstract

Large Language Models (LLMs), a class of contemporary artificial intelligence systems, are increasingly used in scientific practice to support research workflows, accelerate discovery, and automate routine administrative tasks. This contribution identifies and analyzes three underexplored aspects of LLM adoption in scientific research. The first aspect concerns the uneven adoption of LLMs among scientists and the inconsistent application of established best practices. The second examines how LLMs can be employed to improve the robustness and reproducibility of scientific practices. The third addresses institutional strategies by which large scientific organizations—such as universities and research networks—can reduce dependence on commercial technology providers while increasing trust in LLM-based systems.The findings found in our contribution are the partly summarization of István Bozsó’s experiences serving in the role of an “AI ambassador” at the Institute of Earth Phyisics and Space Science (EPSS) of the Hungarian Research Network (HUN-REN).In our experience many scientists are still skeptical of using LLMs in any capacity or lack the time to invest in learning these technologies. These barriers are primarily sociotechnical rather than purely technical in nature, and they require, on one hand materials that teach best-practices and show motivating examples for using LLMs, on the other hand services provided by research organisations.Recent advances in open-weight LLMs enable self-hosting within institutional computing infrastructures, which means research institutes can run these models on their own hardware and thereby ensuring that sensitive data and research materials remain within the organization’s controlled digital environment. This also ensures that the LLM usage stays independent of Large Technology corporations and builds trust with colleagues.Regarding motivating examples, we wish to focus on two areas which can be addressed with the help of LLMs. One area is scientific communication. LLMs can easily generate materials (primarily text, sound and video) which can be used to inform the wider public on new scientific discoveries and push back against misinformation and disinformation campaigns. The involment of scientists is paramount in the review and finalization of such materials to ensure they represent accurate scientific information.The other area is scientific programming. Many scientists are not trained as professional software engineers and often lack the time and background to apply software development best practices. In many cases this results in software artifacts that are fragile, difficult to reproduce, and challenging to maintain and usually only work on the machine of the researcher who developed the package. LLMs can help out in these situations by suggesting and even implementing best practices and giving programming advice to the researcher during the development of the scientific code.The common theme in these examples is that the LLM is not meant to replace the scientist but enhance their capabilities with the goal of increasing the robustness, transparency, and sustainability of the scientific research process.

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