{"id":76568,"date":"2025-04-16T00:34:28","date_gmt":"2025-04-16T04:34:28","guid":{"rendered":"https:\/\/world.edu\/?p=76568"},"modified":"2025-04-16T00:34:28","modified_gmt":"2025-04-16T04:34:28","slug":"a-weird-phrase-is-plaguing-scientific-papers-and-we-traced-it-back-to-a-glitch-in-ai-training-data","status":"publish","type":"post","link":"https:\/\/world.edu\/a-weird-phrase-is-plaguing-scientific-papers-and-we-traced-it-back-to-a-glitch-in-ai-training-data\/","title":{"rendered":"A weird phrase is plaguing scientific papers \u2013 and we traced it back to a glitch in AI training data"},"content":{"rendered":"

\"\"<\/p>\n

Earlier this year,\u00a0scientists discovered a peculiar term<\/a>\u00a0appearing in published papers: \u201cvegetative electron microscopy\u201d.<\/p>\n

This phrase, which sounds technical but is actually nonsense, has become a \u201cdigital fossil\u201d \u2013 an error preserved and reinforced in artificial intelligence (AI) systems that is nearly impossible to remove from our knowledge repositories.<\/p>\n

Like biological fossils trapped in rock, these digital artefacts may become permanent fixtures in our information ecosystem.<\/p>\n

The case of vegetative electron microscopy offers a troubling glimpse into how AI systems can perpetuate and amplify errors throughout our collective knowledge.<\/p>\n

A bad scan and an error in translation<\/strong><\/h4>\n

Vegetative electron microscopy appears to have originated through a remarkable coincidence of unrelated errors.<\/p>\n

First,\u00a0two<\/a>\u00a0papers<\/a>\u00a0from the 1950s, published in the journal Bacteriological Reviews, were scanned and digitised.<\/p>\n

However, the digitising process erroneously combined \u201cvegetative\u201d from one column of text with \u201celectron\u201d from another. As a result, the phantom term was created.<\/p>\n

\"\"

Excerpts from scanned papers show how incorrectly parsed column breaks lead to the term \u2018vegetative electron micro\u2026\u2019 being introduced. Bacteriological Reviews<\/p><\/div>\n

Decades later, \u201cvegetative electron microscopy\u201d turned up in some Iranian scientific papers. In\u00a02017<\/a>\u00a0and\u00a02019<\/a>, two papers used the term in English captions and abstracts.<\/p>\n

This\u00a0appears to be due to a translation error<\/a>. In Farsi, the words for \u201cvegetative\u201d and \u201cscanning\u201d differ by only a single dot.<\/p>\n

\"\"

Screenshot from Google Translate showing the similarity of the Farsi terms for \u2018vegetative\u2019 and \u2018scanning\u2019. Google Translate<\/p><\/div>\n

An error on the rise<\/strong><\/h4>\n

The upshot? As of today, \u201cvegetative electron microscopy\u201d appears in\u00a022 papers, according to Google Scholar<\/a>. One was the subject of a contested\u00a0retraction from a Springer Nature journal<\/a>, and Elsevier\u00a0issued a correction<\/a>\u00a0for another.<\/p>\n

The term also appears in news articles discussing subsequent\u00a0integrity investigations<\/a>.<\/p>\n

Vegetative electron microscopy began to appear more frequently in the 2020s. To find out why, we had to peer inside modern AI models \u2013 and do some archaeological digging through the vast layers of data they were trained on.<\/p>\n

Empirical evidence of AI contamination<\/strong><\/h4>\n

The large language models behind modern AI chatbots such as ChatGPT are \u201ctrained\u201d on huge amounts of text to predict the likely next word in a sequence. The exact contents of a model\u2019s training data are often a closely guarded secret.<\/p>\n

To test whether a model \u201cknew\u201d about vegetative electron microscopy, we input snippets of the original papers to find out if the model would complete them with the nonsense term or more sensible alternatives.<\/p>\n

The results were revealing. OpenAI\u2019s GPT-3 consistently completed phrases with \u201cvegetative electron microscopy\u201d. Earlier models such as GPT-2 and BERT did not. This pattern helped us isolate when and where the contamination occurred.<\/p>\n

We also found the error persists in later models including GPT-4o and Anthropic\u2019s Claude 3.5. This suggests the nonsense term may now be permanently embedded in AI knowledge bases.<\/p>\n

\"\"

Screenshot of a command line program showing the term \u2018vegetative electron microscopy\u2019 being generated by GPT-3.5 (specifically, the model gpt-3.5-turbo-instruct). The top 17 most likely completions of the provided text are \u2018vegetative electron microscopy\u2019, and these suggestions are 2.2 times more likely than the next most likely prediction. OpenAI<\/p><\/div>\n

By comparing what we know about the training datasets of different models, we identified the\u00a0CommonCrawl<\/a>\u00a0dataset of scraped internet pages as the most likely vector where AI models first learned this term.<\/p>\n

The scale problem<\/strong><\/h4>\n

Finding errors of this sort is not easy. Fixing them may be almost impossible.<\/p>\n

One reason is scale. The CommonCrawl dataset, for example, is millions of gigabytes in size. For most researchers outside large tech companies, the computing resources required to work at this scale are inaccessible.<\/p>\n

Another reason is a lack of transparency in commercial AI models. OpenAI and many other developers refuse to provide precise details about the training data for their models. Research efforts to reverse engineer some of these datasets have also been\u00a0stymied by copyright takedowns<\/a>.<\/p>\n

When errors are found, there is no easy fix. Simple keyword filtering could deal with specific terms such as vegetative electron microscopy. However, it would also eliminate legitimate references (such as this article).<\/p>\n

More fundamentally, the case raises an unsettling question. How many other nonsensical terms exist in AI systems, waiting to be discovered?<\/p>\n

Implications for science and publishing<\/strong><\/h4>\n

This \u201cdigital fossil\u201d also raises important questions about knowledge integrity as AI-assisted research and writing become more common.<\/p>\n

Publishers have responded inconsistently when notified of papers including vegetative electron microscopy. Some have retracted affected papers, while others defended them. Elsevier notably\u00a0attempted to justify the term\u2019s validity<\/a>\u00a0before\u00a0eventually issuing a correction<\/a>.<\/p>\n

We do not yet know if other such quirks plague large language models, but it is highly likely. Either way, the use of AI systems has already created problems for the peer-review process.<\/p>\n

For instance, observers have noted the rise of \u201ctortured phrases<\/a>\u201d used to evade automated integrity software, such as \u201ccounterfeit consciousness\u201d instead of \u201cartificial intelligence\u201d. Additionally,\u00a0phrases<\/a>\u00a0such as \u201cI am an AI language model\u201d have been found in other retracted papers.<\/p>\n

Some automatic screening tools such as\u00a0Problematic Paper Screener<\/a>\u00a0now flag vegetative electron microscopy as a warning sign of possible AI-generated content. However, such approaches can only address known errors, not undiscovered ones.<\/p>\n

Living with digital fossils<\/strong><\/h4>\n

The rise of AI creates opportunities for errors to become permanently embedded in our knowledge systems, through processes no single actor controls. This presents challenges for tech companies, researchers, and publishers alike.<\/p>\n

Tech companies must be more transparent about training data and methods. Researchers must find new ways to evaluate information in the face of AI-generated convincing nonsense. Scientific publishers must improve their peer review processes to spot both human and AI-generated errors.<\/p>\n

Digital fossils reveal not just the technical challenge of monitoring massive datasets, but the fundamental challenge of maintaining reliable knowledge in systems where errors can become self-perpetuating.<\/p>\n

Author Bios:<\/strong> Aaron J. Snoswell is a Research Fellow in AI Accountability, Kevin Witzenberger is also a Research Fellow, GenAI Lab and Rayane El Masri is a PhD Candidate, GenAI Lab all at the Queensland University of Technology<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Earlier this year,\u00a0scientists discovered a peculiar term\u00a0appearing in published papers: \u201cvegetative electron microscopy\u201d. This phrase, which sounds technical but is actually nonsense, has become a \u201cdigital fossil\u201d \u2013 an error preserved and reinforced in artificial intelligence (AI) systems that is nearly impossible to remove from our knowledge repositories. Like biological fossils trapped in rock, these […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[434804],"tags":[435325],"coauthors":[11733],"class_list":["post-76568","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","tag-digital-fossil"],"acf":[],"yoast_head":"\nA weird phrase is plaguing scientific papers \u2013 and we traced it back to a glitch in AI training data - World leading higher education information and services<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/world.edu\/a-weird-phrase-is-plaguing-scientific-papers-and-we-traced-it-back-to-a-glitch-in-ai-training-data\/\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:locale\" content=\"en_US\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:type\" content=\"article\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:title\" content=\"A weird phrase is plaguing scientific papers \u2013 and we traced it back to a glitch in AI training data - World leading higher education information and services\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:description\" content=\"Earlier this year,\u00a0scientists discovered a peculiar term\u00a0appearing in published papers: \u201cvegetative electron microscopy\u201d. 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