Q: It is foreseeable that the technology of LLM2CLIP will be of great significance in expanding CLIP's support for more modal data. As far as the article is concerned, LLM2CLIP has surprisingly improved CLIP's adaptability to cross-language and long text tasks. At the same time, it also proposes application possibilities for higher-dimensional data modalities such as audio and video. Of course, this puts forward further requirements for LLM2CLIP's adaptation strategy and fine-tuning methods. Based on your team's current understanding of LLM2CLIP, what additional challenges will arise, for example, the feature space alignment problem of high-dimensional modalities?
A: To be honest, we’re already exploring a video-based version of LLM2CLIP, including scaling up both the dataset size and model parameters by several orders of magnitude. Please stay tuned for our future updates, and if you’re interested, we’d be happy to discuss this further!
Here are some additional challenges I see in this area:
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Enhancing the Supervisory Signal in Contrastive Learning: While LLMs have a strong capability to understand text, providing valuable and rich textual information is equally critical. For instance, for video tasks, we could enrich the input with denser captions, prompts, or instructions. These could provide more complex and detailed information for the LLM to interpret, thereby enabling it to better guide the construction of the cross-modal space.
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Expanding Contrastive Learning Loss Across Dimensions: Contrastive learning losses can be applied across various dimensions, such as the temporal dimension in video data. Different prompts provided to the LLM could be designed to guide and control the training process in these additional dimensions, further strengthening the multimodal representations.
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Tackling Complex Temporal Logic in Videos: The challenges in video understanding often involve designing solutions for complex temporal relationships over extended time spans. Here, we could incorporate self-play techniques using the LLM to introduce tasks and increase the complexity of the training objectives. This might involve designing scenarios where the LLM can simulate and reason about sequences, further enhancing its learning.
Q: What a groundbreaking paper on LLM2CLIP! The innovative integration of large language models with CLIP to enhance cross-modal representation learning is truly inspiring. The performance improvements demonstrated, particularly in long-text and short-text retrieval tasks, are impressive and have significant implications for the field of multimodal AI.
My admiration for your work encourages me to inquire about the potential applications of LLM2CLIP in more specialized domains, such as medicine or law, where the precision and expertise of textual understanding are paramount. Therefore, I am curious to know if LLM2CLIP has been tested or if there are plans to test it with domain-specific texts that require a high degree of accuracy and proficiency.
Looking forward to your insights on this matter and how LLM2CLIP might be adapted or extended to meet the challenges of these specialized fields!
A: Your idea is fantastic, and in fact, we have had similar thoughts. I believe there is significant potential in working on specialized fields, and here are my reasons:
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Limited Data, High Impact: Our work focuses on fine-tuning pre-trained CLIP models with very limited data for LLM2CLIP, ranging from 3M to 60M. Compared to the 1-2B data commonly used in CLIP pre-training, this is a small amount, yet it has already demonstrated substantial performance improvements. If we focus on specialized fields, we could leverage limited domain-specific data to train the model exceptionally well in a specific knowledge area. This approach could potentially resolve issues like perception or cognition hallucinations in related multimodal domains entirely.
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Leveraging LLM Knowledge as Data Augmentation: Certain specialized fields, such as medical reports, often suffer from a lack of data. Here, the knowledge encoded in LLMs can serve as an excellent data augmenter due to their access to open-world knowledge over time.
We look forward to collaborating with you to push the boundaries of multimodal domains!
BTW, we plan to release scaled-up LLM2CLIP models (10-100x larger) next quarter. These models will inherit our general-purpose parameters, potentially making them even more powerful. Please stay tuned to our GitHub!
Q: Thank you so much for such an outstanding work. I have a couple of questions regarding the fine-tuning process described in Section 3.2, particularly around the integration of loss functions and datasets:
In the paper, two loss functions are mentioned: SimCSE loss and Masked Next Token Prediction (MNTP). However, it is unclear whether these two loss functions are used simultaneously during training, or if the training process is split into different phases where each loss is applied separately. Could you please clarify how the losses are used? If they are used together, what are the relative weights assigned to each?
Regarding the datasets, CC-3M and Wikitext-103 are mentioned as part of the training process. It seems a bit unclear how these two datasets are combined in the training phase. Given that Wikitext-103 is a pure language corpus while CC-3M is image-caption based, how are they jointly used during the fine-tuning process? Are they used for different stages or tasks?
Looking forward to your insights on this!
A: Thank you for your question. I’m glad to clarify.
Loss Functions Integration: We use the supervised SimCSE loss to make different captions of the same image positive samples for each other, while captions of different images serve as negative samples. This loss function is key to our method, allowing the LLM to provide meaningful supervisory signals to the image. However, the Masked Next Token Prediction (MNTP) was an initial stage we employed before using the supervised SimCSE loss; it can be understood as an earlier step in training. We first conduct MNTP, followed by supervised SimCSE loss, in a two-stage process. In practice, MNTP has little impact on the results, so removing it does not affect the conclusions. However, for optimal performance, we still chose to use MNTP before applying supervised SimCSE loss.
Dataset Combination: We indeed mix both pure text and caption datasets. This is because the LLM is initially pre-trained on pure text data, so we aim to retain its original distribution with minimal shift by using the pure text dataset Wikitext-103, which also helps mitigate any bias introduced by captions. Our approach is to mix and shuffle the two datasets and then sample batches normally for training. This is a common and effective practice.
If you have more questions, please feel free to ask.
Q: LLM2CLIP does not bring out significant improvements on ImageNet-1k only or all these zero-shot benchmarks?
Have you ever measured the average caption length between your method and vanilla EVA-02-CLIP? In my opinion, longer text captions do not always bring out improvements.
It's reasonable to improve the performances of VLMs on the SQA and Wizwiz benchmarks while it's strange to drop the performances on the fundamental benchmarks such as MME.
A: We haven’t specifically tested it, and the improvement on ImageNet is indeed not very noticeable. With OpenAI’s CLIP, we can achieve about a one-point improvement, which is relatively modest compared to other retrieval tasks. My guess is that we used a large amount of dense captions, which may cause the model to favor more complex text. However, we have found in experiments that ImageNet performance is strongly correlated with data volume, possibly related to the word distribution used during alignment. We only used 15 million data points for the alignment in LLM fine-tuning. In the next version, we’ll increase the training data for LLM2CLIP by tens of times, so we plan to re-evaluate it then.
The improvement of long captions or dense captions for CLIP is quite limited. Works like LongCLIP (https://arxiv.org/abs/2403.15378) and DCI (https://arxiv.org/abs/2312.08578) specifically address this issue. The problem here is that the original CLIP text encoder lacks the ability to understand such information or handle captions of this length. However, LLM2CLIP, even when trained on a fully short-text dataset, still demonstrates outstanding and leading performance, as shown in Table 5 of the paper.
Q: Hello!
I am very interested in your work, and I encountered some issues during the reproduction process.
How can I replace the original text encoder with the tuned Llama 3 model? I checked the config file LLM2CLIP-EVA02-L-14-336/configuration_evaclip.py, and I noticed that the model parameters for the text encoder remain the same as those in the original CLIP model. This is a bit confusing to me.
If I’m correct, is the run.sh script provided for training CLIP with a frozen Llama 3 encoder?
Looking forward for your reply!
A: We have updated the caption contrastive fine-tuned version of Llama3-8B-CC (https://huggingface.co/microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned) to assist with your retrieval experiments and training of your own CLIP models. Additionally, the parameters for our adapter and projector have been made available in our OpenAI ViT-L repository (https://huggingface.co/microsoft/LLM2CLIP-Openai-L-14-336). The retrieval testing methods are documented in the model card for reference.
Our tests show retrieval performance exceeding the results reported in the paper, and we encourage you to try it out.
Regarding the EVA series of models, there have been precision mismatches during the conversion to Hugging Face, which are currently being fixed. Updates will be released progressively.
Furthermore, we will provide detailed instructions on how to use LLM2CLIP to fine-tune your own CLIP models in about a week—please stay tuned!
Q: Hello!
I am very interested in your work, and I encountered some issues during the reproduction process.
How can I replace the original text encoder with the tuned Llama 3 model? I checked the config file LLM2CLIP-EVA02-L-14-336/configuration_evaclip.py, and I noticed that the model parameters for the text encoder remain the same as those in the original CLIP model. This is a bit confusing to me.
If I’m correct, is the run.sh script provided for training CLIP with a frozen Llama 3 encoder?
Looking forward for your reply!
A: We have updated the caption contrastive fine-tuned version of Llama3-8B-CC (https://huggingface.co/microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned) to assist with your retrieval experiments and training of your own CLIP models. Additionally, the parameters for our adapter and projector have been made available in our OpenAI ViT-L repository (https://huggingface.co/microsoft/LLM2CLIP-Openai-L-14-336). The retrieval testing methods are documented in the model card for reference.
Our tests show retrieval performance exceeding the results reported in the paper, and we encourage you to try it out.
Regarding the EVA series of models, there have been precision mismatches during the conversion to Hugging Face, which are currently being fixed. Updates will be released progressively.
Furthermore, we will provide detailed instructions on how to use LLM2CLIP to fine-tune your own CLIP models in about a week—please stay tuned!
Q: I find the LLM2CLIP approach inspiring as it leverages large language models (LLMs) to enhance cross-modal representation learning. The integration of fine-tuned LLMs as a textual encoder offers substantial improvements over traditional CLIP models. However, I have a few questions and suggestions regarding the methodology and evaluation:
While the paper highlights the efficiency of training using LoRA and freezing LLM gradients, scaling to datasets larger than the 60M configuration or involving multilingual captions could introduce challenges. Could you elaborate on the computational implications if fine-tuning were performed without freezing the LLM gradients?
The contrastive fine-tuning strategy for improving feature discriminability is innovative. However, as mentioned, dense captions from ShareCaptioner may introduce noise or distribution mismatches. Have you explored the impact of using alternative caption-generation methods or real-world noisy datasets?
The use of various datasets like DOCCI and ShareGPT4V provides comprehensive evaluations. However, benchmarks focusing on event understanding, video context, or temporal dependencies could further validate the model's capabilities in real-world multimodal tasks.
Overall, LLM2CLIP presents a significant advancement in multimodal learning, setting a foundation for future enhancements in cross-modal representation tasks.
A: We opened the latter layers of the network based on the GPU memory we could accommodate but did not observe significant performance improvements, so we decided not to continue this way. CLIP training relies heavily on batch size, and opening the LLM would compromise the batch size, which could have a negative impact. Additionally, keeping the LLM fixed is actually quite reasonable since our goal is to align the visual model with the correct textual modality. Now that we have access to more abundant computational resources, we plan to conduct more experiments in this area to provide answers for the community.
We have tried the Recaption-1B dataset (https://github.com/UCSC-VLAA/Recap-DataComp-1B) labeled using Llava 1.5, but its performance was not as good as ShareCaptioner 4V. Real-world noisy datasets essentially align with the conclusion in Table 5 of our paper, specifically the 0% short caption results, which show that they underperform compared to using VLLMs for recaptioning. In our next version, we plan to incorporate a large volume of GPT-4o recaptioned results—please stay tuned!
Thank you for your excellent suggestions. Do you have any specific benchmarks you would recommend? We’d be happy to test them.
We truly appreciate your recognition and look forward to contributing more valuable models and knowledge to the community in the future.
Q: This is a really interesting paper that presents a compelling approach to improving visual representation learning by effectively integrating the power of LLMs with CLIP. The entire paper feels well motivated, thoroughly researched, and clearly presented - a truly excellent contribution to the field!
I am a bit curious that given the importance of CLIP in guiding the image generation process of diffusion models, and the enhancement of CLIP's image-text understanding capabilities by LLM2CLIP demonstrated in the paper, can integrating LLM2CLIP into the training and inference of a diffusion model bring a boost in the text-to-image domain? For example, FLUX and Stable Diffusion 3 series show significant improvement in following natural language prompts than previous diffusion models, and I think LLM2CLIP will bring further improvements.
Thank you for your innovative work and significant contribution to the field of multimodal learning!
A: Yes, we have also considered that incorporating LLM2CLIP into image-text generative models could enable more complex and precise control, and we believe there is great potential in this direction. In fact, we’ve already conducted some initial experiments, which indicate that LLM2CLIP’s llama3 performs significantly better than a standard llama3 when simply integrated with Stable Diffusion 3. However, we haven’t had the chance to explore this further in depth yet. We might delve into this more thoroughly in the future. Thank you for recognizing our work!