Securing Trust in ChatGPT: Quality Control and the Role of Citations

The integrity and reliability of research hinge on the trustworthiness of its sources.?

As Generative AI continues to grow in popularity and continues to evolve, examining the interplay between this technology and research publications underscores the essential role of citations in establishing the credibility of large language models (LLMs). Accurate citations ensure that advancements are grounded in verified knowledge, fostering innovation, and preventing the spread of erroneous information.

The key question is then: how can we ensure this accuracy?

The Technology Trust Gap

Trained on huge corpuses of text, Generative AI applications, such as ChatGPT, can produce impressive and coherent text. These applications form the backbone of advancements in natural language processing (NLP), transforming the way we access and interact with information.

The many researchers who have used AI tools during the last year or so have come to appreciate their efficiencies and power in helping with a variety of tasks across the research and innovation workflows. At the same time, this same cohort has also begun to understand some of the challenges, specifically regarding transparency and trust, these technologies additionally pose.

These challenges, though daunting, are not unfamiliar (search and discovery technologies have faced similar issues before). While new obstacles arise, we also encounter long-standing difficulties with any new technology and information.

New Tech, Old Problems

Citations have proved pivotal in many stages of new technologies and in new ways of communicating and publishing information. Historically, they have been critical in helping to organize information, such as the Web, Wikipedia, and now LLMs. Flash back to the early nineties.

In comes powerful new technology in the form of web browsers and search engines, emerging and changing the world, much like with our current experience with ChatGPT. We’re also starting to have personal computers and be able to interact with massive amounts of information. You can look up anything on anything produced by anything. From AltaVista to Aliweb to Ask Jeeves: these were how we got answers from all these new sources of information from the World Wide Web.

And, of course, those aforementioned challenges existed here.?

How do we know what to trust? Is Jeeves going to give us the best article? Is it the most relevant? Is it something that an unknown someone produced? Or is it from the New York Times?

These issues, stemming from information overload and organization, are very reminiscent of where we are today with LLMs and ChatGPT. And we're still trying to figure out what is the best way to do this right.

Trusting Wikipedia: A Crowdsourced Encyclopedia?

Another example of a time we’ve had to contend with a powerful new technology or a way of looking at information: Wikipedia. While we can take it for granted at this stage, it’s pretty remarkable.?

This is a crowd-sourced encyclopedia by strangers across the world on almost any topic. And yet, in many cases, it's more or less trustworthy.?

So, how are we able to rely on people, qualifications, and credentials unknown, to put “source-able” content up on the web? How has Wikipedia been able to exist in this way, and that we can trust it for the most part?

This query brings us to yet another point: how can we trust ChatGPT??

To learn more about evaluating generative outputs from LLMs, read the full blog here: https://bit.ly/3X2ZSn7

Large Language Model Experience for Researchers

The north star of Scite Assistant is to build out control and trust. Our tool combines the power of large language models with our unique database of Smart Citations to minimize the risk of hallucinations and improve information quality with real references.

Interested in a more guided exploration? Download our Scite Assistant Prompt Guidebook and consider requesting a demo or free trial to see how Scite Assistant can enhance your research workflow.

The future of AI-assisted research is here – it's time to make the most of it!