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+evaluation+methods+typically+focus+on+testing+the+performance+on+some+closed-environment+and+domain-specific+benchmarks+with+human+annotations.+In+this+paper%252C+we+explore+a+novel+unsupervised+evaluation+direction%252C+utilizing+peer-review+mechanisms+to+measure+LLMs+automatically.+In+this+setting%252C+both+open-source+and+closed-source+LLMs+lie+in+the+same+environment%252C+capable+of+answering+unlabeled+questions+and+evaluating+each+other%252C+where+each+LLM%27s+response+score+is+jointly+determined+by+other+anonymous+ones.+To+obtain+the+ability+hierarchy+among+these+models%252C+we+assign+each+LLM+a+learnable+capability+parameter+to+adjust+the+final+ranking.+We+formalize+it+as+a+constrained+optimization+problem%252C+intending+to+maximize+the+consistency+of+each+LLM%27s+capabilities+and+scores.+The+key+assumption+behind+is+that+high-level+LLM+can+evaluate+others%27+answers+more+accurately+than+low-level+ones%252C+while+higher-level+LLM+can+also+achieve+higher+response+scores.+Moreover%252C+we+propose+three+metrics+called+PEN%252C+CIN%252C+and+LIS+to+evaluate+the+gap+in+aligning+human+rankings.+We+perform+experiments+on+multiple+datasets+with+these+metrics%252C+validating+the+effectiveness+of+the+proposed+approach.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.3f350889546a73248f924a9ff56fa8dc%3frik%3d9M7m6Q%252floFHp%252bg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Pico+Research+Model&FORM=IRPRST&ck=3B921C8CE61F1090DC5BF4ACBBDFB98A&selectedIndex=109&itb=0)
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