Martin Májovský, Martin Černý, David Netuka, Tomáš Mikolov
{"title":"Perfect detection of computer-generated text faces fundamental challenges","authors":"Martin Májovský, Martin Černý, David Netuka, Tomáš Mikolov","doi":"10.1016/j.xcrp.2023.101769","DOIUrl":null,"url":null,"abstract":"<p>Recent advancements in large language models (LLMs) have sparked a debate on the detection of artificial intelligence (AI)-generated text, a concern especially prevalent among academic institutions and publishers. While current detection tools claim high accuracy rates, some studies point to their unreliability. This paper contends that efforts to detect AI writing are fundamentally flawed because improved detection capabilities could inadvertently refine AI writing tools, leading to a technological arms race. Moreover, the rapid evolution of LLMs means detection methods may quickly become obsolete. We propose a focus on ethical guidelines rather than outright prohibitions, emphasizing that technological solutions should complement, not replace, the core ethical principles of scientific publishing.</p>","PeriodicalId":9703,"journal":{"name":"Cell Reports Physical Science","volume":"39 1","pages":""},"PeriodicalIF":7.9000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Physical Science","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1016/j.xcrp.2023.101769","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Recent advancements in large language models (LLMs) have sparked a debate on the detection of artificial intelligence (AI)-generated text, a concern especially prevalent among academic institutions and publishers. While current detection tools claim high accuracy rates, some studies point to their unreliability. This paper contends that efforts to detect AI writing are fundamentally flawed because improved detection capabilities could inadvertently refine AI writing tools, leading to a technological arms race. Moreover, the rapid evolution of LLMs means detection methods may quickly become obsolete. We propose a focus on ethical guidelines rather than outright prohibitions, emphasizing that technological solutions should complement, not replace, the core ethical principles of scientific publishing.
期刊介绍:
Cell Reports Physical Science, a premium open-access journal from Cell Press, features high-quality, cutting-edge research spanning the physical sciences. It serves as an open forum fostering collaboration among physical scientists while championing open science principles. Published works must signify significant advancements in fundamental insight or technological applications within fields such as chemistry, physics, materials science, energy science, engineering, and related interdisciplinary studies. In addition to longer articles, the journal considers impactful short-form reports and short reviews covering recent literature in emerging fields. Continually adapting to the evolving open science landscape, the journal reviews its policies to align with community consensus and best practices.