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+to+evaluate+such+failures%252C+covering+15+typologically+diverse+languages+with+existing+and+newly-created+English+and+multilingual+prompts.+We+evaluate+a+range+of+LLMs+on+monolingual+and+cross-lingual+generation+reflecting+practical+use+cases%252C+finding+that+Llama+Instruct+and+Mistral+models+exhibit+high+degrees+of+language+confusion+and+even+the+strongest+models+fail+to+consistently+respond+in+the+correct+language.+We+observe+that+base+and+English-centric+instruct+models+are+more+prone+to+language+confusion%252C+which+is+aggravated+by+complex+prompts+and+high+sampling+temperatures.+We+find+that+language+confusion+can+be+partially+mitigated+via+few-shot+prompting%252C+multilingual+SFT+and+preference+tuning.+We+release+our+language+confusion+benchmark%252C+which+serves+as+a+first+layer+of+efficient%252C+scalable+multilingual+evaluation+at+https%3a%252F%252Fgithub.com%252Ffor-ai%252Flanguage-confusion.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.2c37c498368a0265149674eac28111d7%3frik%3dqmy8EItLGAcLEw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Misundertsnading+Different+Technical+Language&simid=608033886976901036&FORM=IRPRST&ck=9EB966AE02B9AB21AB088BF44EBA4413&selectedIndex=14&itb=0)
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