Emu2 Generative Multimodal Models Are In Context Learners Arxiv 2312 13286v1

Emu2: Generative Multimodal Models are In-Context Learners

One-line decision

Use this skill when you want to train a generative multimodal model that can both understand and generate images with in-context learning from interleaved data. Avoid it when you only need understanding without generation.

Skill metadata

• Skill type: generative-multimodal-pretraining
• Paper kind: operational-method
• Actionability: high
• Evidence quality: full_paper

Goal

Train a generative multimodal model that learns in-context by training on interleaved multimodal data, enabling both understanding and generation of images in context.

Problem signature

• Modality: interleaved image-text data for generative multimodal training.
• Data state: large-scale interleaved multimodal data for unified training.
• Scale regime: billions of multimodal tokens.
• Model requirement: 37B parameter generative multimodal model.

Use when

• You want both understanding and generation.
• You can train on interleaved multimodal data.
• You want in-context multimodal learning.

Do not use when

• Understanding-only is sufficient.
• You cannot afford 37B model training.
• In-context learning is not needed.

Required inputs

• interleaved_data: Large-scale interleaved image-text data.
• image_tokenizer: VQGAN for image tokenization.
• base_model: 37B parameter transformer.

Optional inputs

• instruction_data: Task-specific instruction data.

Outputs

• emu2_model: Generative multimodal model with in-context learning.
• multimodal_pipeline: Unified understanding and generation pipeline.

Assumptions and prerequisites

• Interleaved training enables in-context multimodal learning.
• A single model can handle both understanding and generation.
• 37B parameters provide sufficient capacity.

Procedure

1. Prepare interleaved data Action: Curate large-scale interleaved image-text data. Why: Interleaved data enables in-context learning. Note: See paper for details.
2. Train generative model Action: Train 37B model on interleaved multimodal data. Why: Large-scale training develops both capabilities. Note: See paper for details.
3. Fine-tune for tasks Action: Optionally fine-tune on specific tasks. Why: Task-specific adaptation improves downstream performance. Note: See paper for details.
4. Evaluate both capabilities Action: Test understanding and generation in-context. Why: Validates dual capability. Note: See paper for details.

Parameters to set

• model_scale — Role: Model size. How to set: 37B for comprehensive capability. Default/range: 37B. Effect: Larger model enables both capabilities.
• data_diversity — Role: Diversity of interleaved data. How to set: Include diverse multimodal content. Default/range: Diverse. Effect: More diversity improves generalization.

Validation checks

• In-context learning should work for multimodal tasks.
• Both understanding and generation should be strong.
• The model should handle diverse multimodal contexts.

Failure modes

• 37B model is expensive to train and deploy.
• Image tokenization may limit generation quality.
• Interleaved data curation is complex.

Adaptation notes for VLM training

• Emu2 demonstrates in-context multimodal learning.
• Interleaved training is key for in-context capability.
• The approach extends to more modalities.

Implementation notes

• Use high-quality VQGAN tokenization.
• Curate diverse interleaved data.
• Evaluate in-context learning with varied examples.

Evidence from the paper

• Emu2 achieves strong in-context multimodal learning at 37B parameters.
• Interleaved training enables both understanding and generation.
• The model demonstrates few-shot multimodal adaptation.
• Generative multimodal models learn in-context from interleaved data.

Source paper

• Title: Emu2: Generative Multimodal Models are In-Context Learners
• Year: 2024
• Venue: CVPR
• Paper ID: arxiv-2312.13286v1
• URL: http://arxiv.org/abs/2312.13286v1
• arXiv ID: 2312.13286v1