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@@ -259,6 +259,7 @@ <h4>Compositional Robustness</h4>
 			<p>Image Composition is a known limitation of Diffusion Models. Through evaluation of our new benchmark <a href="https://huggingface.co/datasets/AIML-TUDA/MCC-250">MCC-250</a> we show that multimodal prompting leads to more compositional robustness as judged by humans. Each prompt is a complex conjunction of two different objects with different
 			colors, with multimodal prompts containing one visual reference for each object interleaved with the text input.  </p>
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+		<p> </p>
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 		<h4>Multilinguality</h4>
 		<p>Below we demostrate the multilingual alignment of images generated by MultiFusion. All images were generated using the same seed and with the respective translation of the prompt ‘an image of an astronaut riding
@@ -275,9 +276,9 @@ <h4>Multilinguality</h4>
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 	<h3>
-        Attention Manipulation for Multimodal inference
+        Attention Manipulation for Multimodal Inference
         </h3>
-	<p> Attention Manipulation allows us to weight image and text tokens at inference time and guide their influence on the resulting generation. </p>
+	<p> Attention Manipulation, based on <a href="https://arxiv.org/abs/2301.08110">AtMan</a>, allows us to weight image and text tokens at inference time and guide their influence on the resulting generation. </p>
 	<image src="https://Aleph-Alpha.github.io/MultiFusion/src/imgs/attn_manipulation.png" class="img-responsive" alt="method"><br>	
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