DataCite

Kristian Garza

Academic Publishing web forms meet your demise: The unstoppable rise of large language models (ChatGPT)     As we enter the age of artificial intelligence, it’s worth considering how large language models will revolutionize how we interact with websites and applications. Web forms have been the dominant method for users to input data and complete tasks online for decades.

Academic Publishing web forms meet your demise: The unstoppable rise of large language models…

In the winding world of academic research, the quest for an appropriate repository to store and share scholarly data has been akin to a modern-day odyssey. Traditionally, this journey involved wading through static pages and search indexes, a process both time-consuming and fraught with uncertainty.

The Impact of Language User Interfaces on Finding Scholarly Repositories

Picture, if you will, the labyrinthine world of academic information management, where metadata schema mapping serves as a vital underpinning for the exchange and intermingling of data across diverse platforms and systems. This arena has long been dominated by the venerable metadata schema crosswalk, which, though serviceable, has begun to show its age.

ParrotGPT: On the Advantages of Large Language Models Tools for Academic Metadata Schema Mapping

Large language models have been making waves in artificial intelligence for a good reason. These models have the ability to understand and generate human language, which has far-reaching implications for the future of technology. One area where these models have a significant impact is in the realm of software development.

Revolutionizing Metadata Schema Mapping with ChatGPT

Stories by Kristian Garza on Medium

In the winding world of academic research, the quest for an appropriate repository to store and share scholarly data has been akin to a modern-day odyssey. Traditionally, this journey involved wading through static pages and search indexes, a process both time-consuming and fraught with uncertainty. However, the advent of Language User Interfaces (LUIs) has heralded a paradigm shift, reshaping the landscape of repository discovery and accessibility.

![From left to right: The Evolution of Finding Scholarly Repositories in the Age of LLMs](https://cdn-images-1.medium.com/max/1024/1*WFEnG146R-wCpkUJejrRrw.png)

### The Rise of Language User Interfaces

As with many quests in the realm of academia, various projects were spun to address this problem, each achieving moderate success. These endeavours, while commendable, often lacked the sophistication to fully grasp and cater to the diverse and evolving needs of the scholarly community. It was in this setting that Language User Interfaces (LUIs) emerged as a groundbreaking development.

The emergence of tools like the [Repository Finder](https://doi.org/10.5438/wday-8958) underscores the growing need for sophisticated repository discovery methods. Similarly, the [FAIRsFAIR initiative](https://www.fairsfair.eu/repository-discovery-datacite-commons) has contributed significantly to this landscape by integrating repository information into DataCite Commons, thus improving the discovery of FAIR-enabling repositories.

Nevertheless, researchers faced significant challenges with these methods of finding repositories. These methods, primarily involving static lists and search indexes, had several drawbacks:

1.  Search indexes were limited in understanding the nuanced and context-specific needs of researchers, resulting in less relevant recommendations.
2.  Static lists of repositories offered little to no customization to individual researchers' needs, often leading to a mismatch between the repository features and the researchers' specific requirements.
3.  Navigating through extensive lists or indexes is not only tedious but also time-consuming, potentially diverting valuable time from actual research activities.

In contrast, LUIs address these limitations by offering a more personalized, efficient, and context-aware approach to repository discovery.

[Language User Interfaces (LUIs) are a subset of human-computer interaction where users engage with digital systems through natural language, either textually or vocally](https://www.youtube.com/watch?v=l5mG4z343qg&list=PL1T8fO7ArWleyIqOy37OVXsP4hFXymdOZ). This approach stands in stark contrast to traditional graphical user interfaces (GUIs), where interaction is based on visual elements and direct manipulation. LUIs leverage advanced computational linguistics and Large Language Models, such as GPT-4, LLama and Bard, to interpret, process, and respond to user queries conversationally. This human-centric design paradigm makes them particularly well-suited for complex tasks like navigating the diverse landscape of scholarly repositories, where the subtleties of human language and specific domain knowledge play a critical role.

### Emerging LUI Tools in Scholarly Data Management

[ChatGPT - Goldie the Scholar Retriever](https://chat.openai.com/g/g-nnBbYTm6f-goldie-the-scholar-retriever)

Among these developments is Goldie, the scholar retriever. Goldie, using the combination of OpenAI's GPT-4 Large Language Model and RAG (Retrieval-Augmented Generation), provides a conversational interface for guiding you to find a scholarly repository. This service transcends traditional boundaries by offering a conversational interface where researchers articulate their needs in natural language, and in return, receive tailored recommendations for suitable repositories. While it's a notable advancement, it's one of many tools contributing to the evolution of scholarly data management, helping to offer more personalized repository recommendations. Other examples include the [Dimensions AI assistant](https://www.dimensions.ai/products/all-products/dimensions-ai-assistant/), [Scholar AI](https://scholar-ai.net/), and many others.

<figure>
<img src="https://cdn-images-1.medium.com/max/1024/1*_Pxf8zFfwGIJ2QXfUNe--w.png" alt="UI with suggestions in directly from chatGPT." />
<figcaption>Goldie's suggestion on its landing page.</figcaption>
</figure>

![Goldie conversation for crystallographic data structure repositories.](https://cdn-images-1.medium.com/max/1024/1*B2_mahags6oBvrY-GnFvMw.png)

### Technological Innovations and Enhanced Accessibility

Moreover, the application of OpenAI GPT in the backend of services like Goldie represents a leap in understanding and responding to complex, context-driven queries. Coupled with RAG, it ensures that the responses are not only contextually relevant but also enriched with information retrieved from a vast corpus of scholarly repository knowledge.

While the evolution from static pages and search indexes to sophisticated LUIs like Goldie marks a significant milestone, it's also a journey that continues to evolve. As the creator of Goldie, I am keenly aware that the development of such tools is an iterative process, one that flourishes with community input and collaboration.

Therefore, I extend an invitation to the scholarly community to engage with Goldie. [Your feedback,](https://www.inmedias-res.org/goldie) experiences, and insights are invaluable in shaping Goldie to better meet the diverse and evolving needs of the global research community. Together, we can ensure that the future of scholarly communication is not only more accessible and efficient but also collaboratively enriched.

[Kristian\'s Substack \| Kristian Garza \| Substack](https://kjgarza.substack.com/embed)

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=176c42cd7c4d){width="1" height="1"}
