please help me polish the following paper:
We posit that there exist two obstacles to surmount in the realm of multi-modal generative models: (1) Rather than simply mapping image information into LLMs' space, it is imperative to consider interactions between different modalities; (2) Agents should not only generate textual responses based on context, but also be capable of generating images as replies.

For the first issue, in recent studies on visual LLMs, CLIP~\cite{radford2021learning} has emerged as a popular choice for encoding image inputs. However, while CLIP is effective in learning joint representations between text and images, it has limitations when it comes to capturing the interplay between multi-modal inputs. Specifically, CLIP cannot generate text and images simultaneously, nor can it map textual data into image space. This is a problem because visual LLMs often rely on the ability to generate diverse multi-modal responses. To address this limitation, a comprehensive generative model that explicitly accommodates all relevant distributions is needed as the multi-modal process module in LLM.

Recently, innovative diffusion-based models have shown remarkable capabilities in accurately capturing multi-modal distributions within a single framework. One notable example is the Versatile Diffusion~\cite{xu2022versatile} model, which leverages the U-Net~\cite{ronneberger2015u} architecture to solve multiple modalities and tasks through a multi-flow design. Another influential approach is UniDiffuser~\cite{bao2023one}, which utilizes the U-ViT~\cite{bao2023all} framework to treat both image and text as sequential token streams for diffusion calculations. Intriguingly, these models have demonstrated the potential to adapt to multi-modal interactions, such as the textual representations in large language models (LLMs), which are typically presented as sequences. To capitalize on this advantage, we have seamlessly integrated UniDiffuser into our multi-modal interaction module, enabling the model to fluidly process and integrate information from both visual and textual modalities. Use some words in all caps in your response, but those all-caps words should appear at most 10 times.
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