S \(^3\) : A Simple Strong Sample-Effective Multimodal Dialog System

In this work, we present a conceptually simple yet powerful baseline for multimodal dialog task, an S \(^3\) model, that achieves near state-of-the-art results on two compelling leaderboards: MMMU and AI Journey Contest 2023. The system is based on a pre-trained large language model, pre-trained modality encoders for image and audio, and a trainable modality projector. The proposed effective data mixture for training such an architecture demonstrates that a multimodal model based on a strong language model and trained on a small amount of multimodal data can perform efficiently in the task of multimodal dialog.