Attributing and situating knowledge cannot be left to language models

Meticulous citation is a marker of well-researched, serious scholarship. Citations do a lot more than attributing credit; they situate claims within the context of existing research and enable scrutiny. When authors cite carelessly, for example by referencing famous figures and articles while overlooking original sources, they make two important errors. First, they credit ideas to the wrong person and, second, they reveal a limited understanding of the relevant scholarship. Misattribution disproportionately harms underrepresented voices, whose work has been shown to be consistently more innovative than that of established researchers. Research led by women tends to be less cited than comparable research led by men. Similarly, research from underrepresented groups, as well as from amateurs and beginners, tends to lead to more breakthroughs and innovation, yet remains less cited than follow-up work from established researchers.

In universities, large language models (LLMs) are now widely used by both students and researchers as part of their research workflow. Students use generative AI tools primarily to “create and improve educational content”, an unsurprising statistic revealed by Anthropic. Researchers resort to them extensively to draft and edit articles — with new policies by journals and conferences increasingly allowing what is called an ethical and responsible use. The use of LLMs in scholarly writing could turbocharge research by making it faster, more effective and less stressful, but risks undoing decades of progress in establishing standards for research excellence7.

In our letter, we argue that LLMs are not authors and cannot bear accountability for human choices made by AI developers and subsequently by users. Researchers who make use of LLM-generated texts must remain responsible for every claim made, and ensure that ideas are attributed to their original authors so that under-credited work is not further erased. Academia as a whole will benefit from it, preserving thought and voice diversity over the levelling-down that LLMs are introducing.