S3: A Simple Strong Sample-effective Multimodal Dialog System
This provides a strong baseline for multimodal dialog tasks, though it appears incremental as it builds on existing pre-trained models and methods.
The authors tackled multimodal dialog systems by proposing S3, a simple baseline model that achieves near state-of-the-art results on the MMMU and AI Journey Contest 2023 leaderboards, demonstrating efficient performance with a small amount of multimodal training data.
In this work, we present a conceptually simple yet powerful baseline for the multimodal dialog task, an S3 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.