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Shortly after OpenAI releasedo1 , its first “ reasoning ” AI model , the great unwashed began noting a queer phenomenon . The model would sometimes begin “ thinking ” in Chinese , Persian , or some other language — even when need a question in English .
Given a trouble to sort out — for example “ How many radius ’s are in the word ‘ strawberry ? ’ ” — o1 would set out its “ thought ” process , arriving at an solution by performing a serial publication of reasoning step . If the dubiousness was written in English , o1 ’s final response would be in English . But the manikin would perform some steps in another language before drawing its conclusion .
“ [ o1 ] arbitrarily started thinking in Chinese halfway through , ” one user on Redditsaid .
“ Why did [ o1 ] indiscriminately bug out thinking in Chinese ? ” a unlike user asked in apost on X. “ No part of the conversation ( 5 + messages ) was in Chinese . ”
Why did o1 pro arbitrarily start thinking in Chinese ? No part of the conversation ( 5 + messages ) was in Chinese … very interesting … aim data influencepic.twitter.com/yZWCzoaiit
— Rishab Jain ( @RishabJainK)January 9 , 2025
OpenAI has n’t supply an explanation for o1 ’s strange behavior — or even acknowledged it . So what might be give-up the ghost on ?
Well , AI expert are n’t sure . But they have a few theories .
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Several on X , including Hugging Face CEO Clément Delangue , alludedto the fact that abstract thought example like o1 are train on datasets hold in a lot of Chinese character . Ted Xiao , a research worker at Google DeepMind , claimed that fellowship including OpenAI use third - political party Taiwanese data labeling services , and that o1 switching to Chinese is an example of “ Chinese linguistic influence on reasoning . ”
“ [ Labs like ] OpenAI and Anthropic utilize [ third - party ] datum labeling services for PhD - spirit level abstract thought data for science , math , and coding , ” Xiao wrote in apost on X. “ [ F]or expert labor availability and cost intellect , many of these data providers are based in China . ”
Labels , also be intimate as tags or annotation , avail models understand and interpret information during the training outgrowth . For example , labels to train an image recognition model might take the form of markings around physical object or captions look up to each person , place , or object depicted in an figure .
written report have shown that biased label can produce coloured models . For example , theaverage annotatoris more likely to label set phrase in African - American Vernacular English ( AAVE ) , the informal grammar used by some Black Americans , as toxic , leading AI toxicity detectors groom on the labels to see AAVE as disproportionately toxic .
Other experts do n’t buy the o1 Chinese data point labeling hypothesis , however . They point out that o1 is just as likely to interchange toHindi , Thai , or a spoken language other than Formosan while badger out a solution .
Rather , these experts say , o1 andother reasoning modelsmight plainly beusing languagesthey line up most efficient to achieve an objective ( orhallucinating ) .
“ The manikin does n’t know what language is , or that language are different , ” Matthew Guzdial , an AI research worker and help professor at the University of Alberta , separate TechCrunch . “ It ’s all just text to it . ”
Indeed , models do n’t forthwith litigate word of honor . They usetokensinstead . Tokenscanbe word of honor , such as “ fantastic . ” Or they can be syllables , like “ devotee , ” “ tantalum , ” and “ tic . ” Or they can even be individual type in words — e.g. “ f , ” “ a , ” “ n , ” “ thymine , ” “ a , ” “ s , ” “ t , ” “ i , ” “ c. ”
Like labeling , tokens can introduce prejudice . For example , many news - to - token translators assume a place in a sentence denote a new Book , despite the fact that not all languages use space to separate word of honor .
Tiezhen Wang , a software engineer at AI inauguration Hugging Face , agrees with Guzdial that abstract thought models ’ language inconsistencies may be explain by association the modelling made during training .
“ By embracing every lingual nuance , we expand the model ’s worldview and take into account it to con from the full spectrum of human knowledge , ” Wangwrotein a post on X. “ For deterrent example , I prefer doing math in Chinese because each digit is just one syllable , which make calculations crisp and efficient . But when it come to topic like unconscious bias , I mechanically change to English , mainly because that ’s where I first learned and absorbed those approximation . ”
Wang ’s theory is plausible . Models are probabilistic auto , after all . condition on many examples , they learn patterns to make prognostication , such as how “ to whom ” in an email typically preface “ it may concern . ”
But Luca Soldaini , a research scientist at the non-profit-making Allen Institute for AI , cautioned that we ca n’t experience for certain . “ This eccentric of observation on a deployed AI organisation is insufferable to back up due to how unintelligible these model are , ” they told TechCrunch . “ It ’s one of the many cases for why transparency in how AI arrangement are built is profound . ”
Short of an solution from OpenAI , we ’re left to muse about why o1 thinks ofsongsin Gallic butsynthetic biologyin Mandarin .
