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Progress toward BenchmarkVisualization
+has+shown+great+promise+for+visual+data+analysis%252C+but+it+remains+a+challenging+task+that+requires+multiple+low-level+implementations%252C+such+as+natural+language+processing+and+visualization+design.+Recent+advancements+in+pre-trained+large+language+models+(LLMs)+are+opening+new+avenues+for+generating+visualizations+from+natural+language.+However%252C+the+lack+of+a+comprehensive+and+reliable+benchmark+hinders+our+understanding+of+LLMs%27+capabilities+in+visualization+generation.+In+this+paper%252C+we+address+this+gap+by+proposing+a+new+NL2VIS+benchmark+called+VisEval.+Firstly%252C+we+introduce+a+high-quality+and+large-scale+dataset.+This+dataset+includes+2%252C524+representative+queries+covering+146+databases%252C+paired+with+accurately+labeled+ground+truths.+Secondly%252C+we+advocate+for+a+comprehensive+automated+evaluation+methodology+covering+multiple+dimensions%252C+including+validity%252C+legality%252C+and+readability.+By+systematically+scanning+for+potential+issues+with+a+number+of+heterogeneous+checkers%252C+VisEval+provides+reliable+and+trustworthy+evaluation+outcomes.+We+run+VisEval+on+a+series+of+state-of-the-art+LLMs.+Our+evaluation+reveals+prevalent+challenges+and+delivers+essential+insights+for+future+advancements.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.46258663d039a710918f5d5a3304057f%3frik%3dDD9fGKpg5Cr28A%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Progress+toward+Benchmark+Visualization&FORM=IRPRST&ck=35195D1AAAC59F6015AAE86BD5E55CAF&selectedIndex=12&itb=0)
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