Over the last decade, there has been a significant increase in supporting reproducible computational research (RCR) [1]. The global adoption of the FAIR principles [2] stands as a key indicator of this trend. Specifically, federal and global research funding agencies have increasingly mandated scientific data and related products, such as code and algorithms, be made Findable, Accessible, Interoperable, and Reusable (FAIR) [2].

Recent developments in Large Language Models (LLMs) have advanced the natural language processing (NLP) studies to a new era [1], [2], [4]–[6]. In generic domains, LLMs have become a key component in wide variety of state-of-the-art NLP tasks. In addition, prompt learning enables LLMs-based models to reach robust performance with much smaller training data.