S3: A Simple Strong Sample-effective Multimodal Dialog System
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2024
- DOI:
- 10.48550/arXiv.2406.18305
- arXiv:
- arXiv:2406.18305
- Bibcode:
- 2024arXiv240618305R
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence