The field of multimodal models is rapidly advancing, with a focus on developing more efficient and effective models for text-image tasks. Researchers are exploring new architectures and training strategies to improve the performance of these models, including the use of multimodal large language models and prompt-based interaction strategies. One of the key challenges in this area is improving compositional generalization, which refers to the ability of models to generalize to new combinations of concepts. Recent studies have highlighted the importance of word co-occurrence statistics in pretraining datasets and have proposed new methods for aligning modalities and leveraging multimodal knowledge. Noteworthy papers include:

• Llama Nemoretriever Colembed, which introduces a unified text-image retrieval model that achieves state-of-the-art performance across multiple benchmarks.
• EPIC, which proposes a novel efficient prompt-based multimodal interaction strategy that reduces computational resource consumption and achieves superior performance on several datasets.
• Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation, which introduces a novel framework for document image machine translation that leverages multimodal large language models to improve translation quality.
• Impact of Pretraining Word Co-occurrence on Compositional Generalization in Multimodal Models, which investigates the impact of word co-occurrence statistics on compositional generalization in multimodal models and proposes new methods for improving generalization.