CLIP is one of the most important multimodal foundational models today. What powers CLIP's capabilities? The rich supervision signals provided by natural language, the carrier of human knowledge, shape a powerful cross-modal representation space. However, with the rapid advancements in large language models LLMs like GPT-4 and LLaMA, the boundaries of language comprehension and generation are continually being pushed. This raises an intriguing question: can the capabilities of LLMs be harnessed to further improve multimodal representation learning? The potential benefits of incorporating LLMs into CLIP are clear. LLMs' strong textual understanding can fundamentally improve CLIP's ability to handle image captions, drastically enhancing its ability to process long and complex texts, a well-known limitation of vanilla CLIP. Moreover, LLMs are trained on a vast corpus of text, possessing open-world knowledge. This allows them to expand on caption information during training, increasing the efficiency of the learning process. In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP's potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer's textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP's visual encoder. Thanks to the LLM's presence, we can now incorporate longer and more complex captions without being restricted by vanilla CLIP's text encoder's context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks.

翻译：CLIP是当今最重要的多模态基础模型之一。是什么赋予了CLIP强大的能力？作为人类知识载体的自然语言所提供的丰富监督信号，塑造了一个强大的跨模态表征空间。然而，随着GPT-4和LLaMA等大型语言模型的快速发展，语言理解与生成的边界不断被突破。这引发了一个引人深思的问题：能否利用LLM的能力来进一步改进多模态表征学习？将LLM融入CLIP的潜在优势是显而易见的。LLM强大的文本理解能力可以从根本上提升CLIP处理图像描述文本的能力，显著增强其处理长而复杂文本的能力，而这是原始CLIP的一个众所周知的局限。此外，LLM在庞大的文本语料库上进行训练，拥有开放世界的知识。这使得它们能够在训练过程中扩展描述信息，从而提高学习过程的效率。在本文中，我们提出了LLM2CLIP，一种利用LLM能力来释放CLIP潜力的新方法。通过在描述文本空间中使用对比学习对LLM进行微调，我们将其文本能力提取到输出嵌入中，显著提升了输出层的文本判别能力。随后，我们设计了一个高效的训练流程，其中微调后的LLM充当CLIP视觉编码器的强大教师。得益于LLM的引入，我们现在可以纳入更长、更复杂的描述文本，而不再受限于原始CLIP文本编码器的上下文窗口和能力限制。我们的实验表明，该方法在跨模态任务上带来了显著的性能提升。