Multimodal Chain-of-Thought (MM-CoT)
| Property | Value |
|---|---|
| Base Model | UnifiedQA-T5-base |
| License | Apache-2.0 |
| Paper | arXiv:2302.00923 |
| Framework | Two-stage training (rationale generation & answer inference) |
What is mm-cot?
MM-CoT is an innovative multimodal reasoning model that combines visual and language processing capabilities to enhance scientific question-answering tasks. It implements a unique two-stage training framework that first generates rationales and then performs answer inference, making it particularly effective for problems requiring both visual and textual understanding.
Implementation Details
The model architecture is built on UnifiedQA-T5-base and uses DETR for vision features processing. The implementation consists of two distinct training stages: rationale generation using the QCM-LE prompt format, and answer inference using the QCMG-A format. The model supports batch processing and includes comprehensive evaluation capabilities.
- Decoupled training framework with separate rationale and answer stages
- Integration of vision features through DETR
- Flexible batch size configuration for training and evaluation
- Customizable output length for different response types
Core Capabilities
- Multimodal reasoning combining vision and language
- Generation of detailed rationales for answers
- Scientific question-answering with visual context
- Flexible inference pipeline for different use cases
Frequently Asked Questions
Q: What makes this model unique?
MM-CoT's uniqueness lies in its two-stage approach to multimodal reasoning, where it first generates explanatory rationales before producing final answers. This chain-of-thought process more closely mimics human reasoning and improves performance on complex scientific questions.
Q: What are the recommended use cases?
The model is particularly well-suited for scientific question-answering tasks that involve both visual and textual elements, such as problems from textbooks, scientific diagrams, or educational materials where understanding both the visual context and textual information is crucial.
