mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs (2307.06930v3)

Abstract: Modular vision-LLMs (Vision-LLMs) align pretrained image encoders with (frozen) LLMs and post-hoc condition LLMs to `understand' the image input. With the abundance of readily available high-quality English image-text data as well as strong monolingual English LLMs, the research focus has been on English-only Vision-LLMs. Multilingual vision-LLMs are still predominantly obtained via expensive end-to-end pretraining, resulting in comparatively smaller models, trained on limited multilingual image data supplemented with text-only multilingual corpora. We present mBLIP, the first Vision-LLM leveraging multilingual LLMs, which we obtain in a computationally efficient manner on consumer-level hardware. To this end, we \textit{re-align} an image encoder previously tuned to an English LLM to a new, multilingual LLM using only a few million multilingual training examples derived from a mix of vision-and-language tasks, which we obtain by machine-translating high-quality English data to 95 languages. On the IGLUE benchmark and XM3600, mBLIP yields results competitive with state-of-the-art models and it greatly outperforms strong English-only Vision-LLMs like Llava 1.5. We release our model, code, and train data at \url{https://github.com/gregor-ge/mBLIP}.

An Analytical Perspective on mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs

The paper "mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs" presents an innovative approach to enhancing modular Vision-LLMs (Vision-LLMs) by transitioning them into the multilingual domain. The core contribution of the paper is the introduction of mBLIP, a multilingual Vision-LLM that is aligned with a pre-trained multilingual LLM through efficient methods involving minimal data and computational resources. This represents a significant stride especially for researchers and practitioners with limited access to extensive computational infrastructure for training large-scale models.

Key Contributions

The principal advancement introduced is the concept of re-aligning a pre-trained image encoder, initially tuned to an English LLM, with a multilingual LLM. This re-alignment is realized by constructing a training set derived from machine-translated data spanning 95 languages, utilizing readily available high-quality English datasets. The training is executed without any additional multilingual text-only data, updating only a small fraction (124 million parameters) of the model, making this process computationally feasible on consumer-grade hardware.

The architecture employed leverages existing modularity in Vision-LLMs. Specifically, it involves a frozen image encoder and a new language-specific alignment through a Q-Former, which acts as an interface between the visual representations and the multilingual LLM. This approach capitalizes on the latent capacities of the original Vision-LLM design while avoiding the resource-intensive requirement of tuning large model components.

Evaluation and Results

mBLIP's performance is evaluated against various benchmarks, such as the IGLUE benchmark and the XM3600 dataset, covering tasks including image captioning, visual question answering (VQA), and visual reasoning across multiple languages. Notably, mBLIP demonstrates competitive results with state-of-the-art large multilingual models like PaLI-X, even outperforming them in certain zero-shot settings despite being trained on significantly less data.

The empirical results indicate mBLIP’s potential in efficiently scaling Vision-LLMs to multilingual contexts across diversified tasks. For image captioning on the XM3600 dataset, mBLIP surpasses PaLI-X in zero-shot inference, which reflects the model's robustness and effective alignment techniques. However, its performance on tasks involving compositionality and understanding, such as in visual inference scenarios, underscores room for further enhancement in capturing nuanced cross-linguistic and cross-modal interactions.

Theoretical and Practical Implications

From a theoretical stance, mBLIP’s design challenges the traditional paradigm of extensive end-to-end multilingual pre-training by proposing an efficient re-alignment mechanism. This suggests a shift towards focusing on strategic model component realignment using task-specific data translation strategies. It underscores the evolving understanding that image encoding can indeed be largely language-agnostic, which, in conjunction with a robust multilingual LLM, allows for enhanced model generalization and performance.

Practically, the development of mBLIP holds significant promise for broadening access to advanced Vision-LLMs for non-English languages, thereby democratizing AI capabilities. This approach can substantially reduce barriers to entry for multilingual AI solutions, particularly where computational resources are a limiting factor, thus fostering innovation in global AI communities.

Speculation on Future Directions

While mBLIP is a promising step, future exploration could focus on refining task-specific adaptation strategies, particularly for handling low-resource languages more effectively. Further work might explore the integration of external multilingual knowledge bases to boost performance in reasoning tasks, or employ more sophisticated data augmentation and translation techniques to bolster the linguistic diversity of training datasets.

Moreover, establishing standardized evaluation metrics and widely recognized benchmarks for multilingual Vision-LLMs can aid in better understanding and comparison among models, guiding future methodological developments in this domain.

In conclusion, the paper presents a compelling case for the efficient adaptation of Vision-LLMs into multilingual environments, laying a groundwork for further research and application in multilingual AI systems across diverse linguistic and cultural contexts.