Tech

Can LLMs understand scientists? | Computer Weekly

Published

9 hours ago

September 19, 2025

Can LLMs understand scientists? | Computer Weekly

The use of large language models (LLMs) as an alternative to search engines and recommendation algorithms is increasing, but early research suggests there is still a high degree of inconsistency and bias in the results these models produce. This has real-world consequences, as LLMs play a greater role in our decision-making choices.

Making sense of algorithmic recommendations is tough. In the past, we had entire industries dedicated to understanding (and gaming) the results of search engines – but the level of complexity of what goes into our online recommendations has risen several times over in just a matter of years. The massive diversity of use cases for LLMs has made audits of individual applications vital in tackling bias and inaccuracies.

Scientists, governments and civil society are scrambling to make sense of what these models are spitting out. A group of researchers at the Complexity Science Hub in Vienna has been looking at one area in particular where these models are being used: identifying scholarly experts. Specifically, these researchers were interested in which scientists are being recommended by these models – and which were not.

Lisette Espín-Noboa, a computer scientist working on the project, had been looking into this before major LLMs had hit the market: “In 2021, I was organising a workshop, and I wanted to come up with a list of keynote speakers.” First, she went to Google Scholar, an open-access database of scientists and their publications. “[Google Scholar] rank them by citations – but for several reasons, citations are biased.”

This meant trawling through pages and pages of male scientists. Some fields of science are simply more popular than others, with researchers having more influence purely due to the size of their discipline. Another issue is that older scientists – and older pieces of research – will naturally have more citations simply for being around longer, rather than the novelty of their findings.

“It’s often biased towards men,” Espín-Noboa points out. Even with more women entering the profession, most scientific disciplines have been male-dominated for decades.

Daniele Barolo, another researcher at the Complexity Science Hub, describes this as an example of the Matthew Effect. “If you sort the authors only by citation counts, it’s more likely they will be read and therefore cited, and this will create a reinforcement loop,” he explains. In other words, the rich get richer.

Espín-Noboa continues: “Then I thought, why don’t I use LLMs?” These tools could also fill in the gaps by including scientists that aren’t on Google Scholar.

But first, they would have to understand whether these were an improvement. “We started doing these audits because we wanted to know how much they knew about people, [and] if they were biased towards men or not,” Espín-Noboa says. The researchers also wanted to see how accurate the tools were and whether they displayed any biases based on ethnicity.