Quotation errors in general science journals

Quotation errors in general science journals

Abstract. Due to the incremental nature of scientific discovery, scientific writing requires extensive referencing to the writings of others. The accuracy

Scientific production in the era of Large Language Models

Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. We find: 1) scientists adopting LLMs to draft manuscripts demonstrate a large increase in paper production, ranging from 23.7-89.3% depending on scientific field and author background, 2) LLM use has reversed the relationship between writing complexity and paper quality, leading to an influx of manuscripts that are linguistically complex but substantively underwhelming, and 3) LLM adopters access and cite more diverse prior work, including books and younger, less-cited documents. These findings highlight a stunning shift in scientific production that will likely require a change in how journals, funding agencies, and tenure committees evaluate scientific works.

Fabricated citations: an audit across 2·5 million biomedical papers

Scientific literature depends on the integrity of its references. Each reference implicitly asserts that a verifiable source exists and supports the claims being made. When references point to non-existent studies, readers, reviewers, and policy makers are unable to evaluate the evidence.

Fabricated citations: an audit across 2·5 million biomedical papers

Scientific literature depends on the integrity of its references. Each reference implicitly asserts that a verifiable source exists and supports the claims being made. When references point to non-existent studies, readers, reviewers, and policy makers are unable to evaluate the evidence.

Hallucinated citations are polluting the scientific literature. What can be done?

Tens of thousands of publications from 2025 might include invalid references generated by AI, a Nature analysis suggests.

Hallucinated citations are polluting the scientific literature. What can be done?

Tens of thousands of publications from 2025 might include invalid references generated by AI, a Nature analysis suggests.

Hallucinated citations are polluting the scientific literature. What can be done?

Tens of thousands of publications from 2025 might include invalid references generated by AI, a Nature analysis suggests.

Citing Less Critically: LLMs Reshape the Rhetoric and Reach of Scientific Citation

Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentioning). As large language models (LLMs) increasingly assist scientific writing, whether they reproduce citations with the same rhetorical intent as humans remains unclear. We introduce a masked-citation task to compare human and LLM-generated citation behavior. For each citation context, an LLM generates a replacement citation sentence, producing a counterfactual corpus directly comparable to human citation. We analyze what, whom, and how models cite, using an LLM-as-a-judge to classify citation intent and a 20-million-edge coauthorship network to measure social distance between cited authors. Across six popular LLMs and 1,746 top NLP conference papers (63k+ contexts, 132k+ citations), three patterns emerge: (1) Compared with human citation, LLMs cite significantly less critically; (2) LLMs over-cite popular and older papers, a tendency amplified for contrasting citations where human writing more often draws on recent, niche work; (3) Whereas humans often cite within their close social network, especially for supporting citations, LLMs tend to draw on more socially distant authors. Together, these differences are double-edged: LLM citation reaches beyond a scholar's close collaborators while being less critical and amplifying visibility bias, reshaping the rhetoric and reach of scientific citation.

Fraudulent citations, blamed on AI hallucinations, are becoming more common in research papers

“Fabricated” citations that do not reference real academic papers are spreading in the literature, polluting the public record of science, a new study found

Incorrect Citation Association for Articles in Online-Only Springer Nature Journals

We show that citation metrics of journal articles in many of the online-only Springer Nature journals and associated ones are distorted, going back to articles from 2001. We find that most likely due to an API response error, there are many incorrect references which typically lead to Article Number 1 of a given Volume. Among others, the issue affects journals such as Scientific Reports, Nature Communications, Communications journals, Cell Death & Disease, Light: Science & Applications, as well as many BMC, Discovery and npj journals. Beyond the negative effect of introducing incorrect reference information, this distorts the citation statistics of articles in these journals, with a few articles being massively over-cited compared to their peers, while many lose citations; e.g. both in Scientific Reports and in Nature Communications, 5 of the 10 top cited articles have article numbers of 1. We validate the distorted statistics by assessing data from multiple scientific literature databases: Crossref, OpenCitations, Semantic Scholar, and the journals' websites. The issue primarily arises from the inconsistent transition from page-based referencing of articles to article number-based referencing, as well as the improper handling of the change in the publisher's article metadata API. It seems that the most pressing problem has been present since approximately 2011, which we estimate affects the citation count of millions of authors.

Editor’s Note: Retraction of article containing fabricated quotations

We are reinforcing our editorial standards following this incident.

Correction of scientific literature: Too little, too late!

The Coronavirus Disease 2019 (COVID-19) pandemic has highlighted the limitations of the current scientific publication system, in which serious post-publication concerns are often addressed too slowly to be effective. In this Perspective, we offer suggestions to improve academia’s willingness and ability to correct errors in an appropriate time frame.

Language agents achieve superhuman synthesis of scientific knowledge

High accuracy RAG for answering questions from scientific documents with citations

AI agents are checking the scientific literature — and spotting decades-old errors

The technology is proving adept at finding faults in decades-old papers and reference databases.

Decolonising Science Reading List

In the last couple of weeks, a lot of people have been asking me about what I’ve been reading as reference points for my commentary on the…