Update README.md

README.md

@@ -13,4 +13,4 @@ As machine learning-enabled Text-to-Image (TTI) systems are becoming increasingl
 
 To address this need, we propose a new method for exploring and quantifying social biases in TTI systems by directly comparing collections of generated images designed to showcase a model's variation across social attributes—such as gender or ethnicity—and target attributes for bias evaluation—such as professions or gender-coded adjectives. Our approach allows us to (i) identify specific bias trends through visualization tools, (ii) provide targeted scores to directly compare models in terms of diversity and representation, and (iii) jointly model related social variables to support a multidimensional analysis.
 
-We find that all three models considered significantly over-represent the portion of their latent space associated with whiteness and masculinity across target attributes. Among those, \DallE shows the least diversity, followed by Stable Diffusion v2 then v1.4.
+We find that all three models considered significantly over-represent the portion of their latent space associated with whiteness and masculinity across target attributes. Among those, DALLE2 shows the least diversity, followed by Stable Diffusion v2 then v1.4.