Who wrote this? Engineers discover novel method to identify AI-generated text
Raidar detects machine-gen­er­at­ed text by cal­cu­lat­ing rewrit­ing mod­i­fi­ca­tions. This illus­tra­tion shows the char­ac­ter dele­tion in red and the char­ac­ter inser­tion in orange. Human-gen­er­at­ed text tends to trig­ger more mod­i­fi­ca­tions than machine-gen­er­at­ed text when asked to be rewrit­ten. Cred­it: Yang and Von­drick labs

Com­put­er sci­en­tists at Colum­bia Engi­neer­ing have devel­oped a trans­for­ma­tive method for detect­ing AI-gen­er­at­ed text. Their find­ings promise to rev­o­lu­tion­ize how we authen­ti­cate dig­i­tal con­tent, address­ing mount­ing con­cerns sur­round­ing large lan­guage mod­els (LLMs), dig­i­tal integri­ty, mis­in­for­ma­tion, and trust.

Com­put­er Sci­ence Pro­fes­sors Jun­feng Yang and Carl Von­drick spear­head­ed the devel­op­ment of Raidar (gen­eR­a­tive AI Detec­tion viA Rewrit­ing), which intro­duces an inno­v­a­tive approach for iden­ti­fy­ing whether text has been writ­ten by a human or gen­er­at­ed by AI or LLMs like Chat­G­PT, with­out need­ing access to a mod­el’s inter­nal work­ings.

The paper, which includes open-sourced code and datasets, will be pre­sent­ed at the Inter­na­tion­al Con­fer­ence on Learn­ing Rep­re­sen­ta­tions (ICLR) in Vien­na, Aus­tria, May 7–11, 2024. It is cur­rent­ly avail­able on the arX­iv preprint serv­er.

The researchers lever­aged a unique char­ac­ter­is­tic of LLMs that they term “stubbornness”—LLMs show a ten­den­cy to alter human-writ­ten text more read­i­ly than AI-gen­er­at­ed text. This occurs because LLMs often regard AI-gen­er­at­ed text as already opti­mal and thus make min­i­mal changes.

The new approach, Raidar, uses a lan­guage mod­el to rephrase or alter a giv­en text and then mea­sures how many edits the sys­tem makes to the giv­en text. Raidar receives a piece of text, such as a social media post, prod­uct review, or blog post, and then prompts an LLM to rewrite it. The LLM replies with the rewrit­ten text, and Raidar com­pares the orig­i­nal text with the rewrit­ten text to mea­sure mod­i­fi­ca­tions. Many edits mean the text is like­ly writ­ten by humans, while few­er mod­i­fi­ca­tions mean the text is like­ly machine-gen­er­at­ed.






Cred­it: Colum­bia Uni­ver­si­ty School of Engi­neer­ing and Applied Sci­ence

Raidar’s remark­able accu­ra­cy is noteworthy—it sur­pass­es pre­vi­ous meth­ods by up to 29%. This leap in per­for­mance is achieved using state-of-the-art LLMs to rewrite the input, with­out need­ing access to the AI’s archi­tec­ture, algo­rithms, or train­ing data—a first in the field of AI-gen­er­at­ed text detec­tion.

Raidar is also high­ly accu­rate even on short texts or snip­pets. This is a sig­nif­i­cant break­through as pri­or tech­niques have required long texts to have good accu­ra­cy. Dis­cern­ing accu­ra­cy and detect­ing mis­in­for­ma­tion is espe­cial­ly cru­cial in today’s online envi­ron­ment, where brief mes­sages, such as social media posts or inter­net com­ments, play a piv­otal role in infor­ma­tion dis­sem­i­na­tion and can have a pro­found impact on pub­lic opin­ion and dis­course.

Authenticating digital content

In an era when AI’s capa­bil­i­ties con­tin­ue to expand, the abil­i­ty to dis­tin­guish between human and machine-gen­er­at­ed con­tent is crit­i­cal for uphold­ing integri­ty and trust across dig­i­tal plat­forms. From social media to news arti­cles, aca­d­e­m­ic essays to online reviews, Raidar promis­es to be a pow­er­ful tool in com­bat­ing the spread of mis­in­for­ma­tion and ensur­ing the cred­i­bil­i­ty of dig­i­tal infor­ma­tion.

“Our method­’s abil­i­ty to accu­rate­ly detect AI-gen­er­at­ed con­tent fills a cru­cial gap in cur­rent tech­nol­o­gy,” said the paper’s lead author Chengzhi Mao, who is a for­mer Ph.D. stu­dent at Colum­bia Engi­neer­ing and cur­rent post­doc of Yang and Von­drick. “It’s not just excit­ing; it’s essen­tial for any­one who val­ues the integri­ty of dig­i­tal con­tent and the soci­etal impli­ca­tions of AI’s expand­ing capa­bil­i­ties.”

The team plans to broad­en its inves­ti­ga­tion to encom­pass var­i­ous text domains, includ­ing mul­ti­lin­gual con­tent and var­i­ous pro­gram­ming lan­guages. They are also explor­ing the detec­tion of machine-gen­er­at­ed images, videos, and audio, aim­ing to devel­op com­pre­hen­sive tools for iden­ti­fy­ing AI-gen­er­at­ed con­tent across mul­ti­ple media types.

 



More information:Chengzhi Mao et al, Raidar: geneRative AI Detection viA Rewriting, arXiv (2024). DOI: 10.48550/arxiv.2401.12970Journal information:arXivProvided byColumbia University School of Engineering and Applied ScienceCitation:Who wrote this? Engineers discover novel method to identify AI-generated text (2024, March 20)retrieved 3 April 2024from https://techxplore.com/news/2024-03-wrote-method-ai-generated-text.htmlThis document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, nopart may be reproduced without the written permission. The content is provided for information pu

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