amazon-science/mm-cot
Mm Cot
This project is a multimodal large language model reasoning framework designed to train and evaluate models in performing chain-of-thought reasoning across text and image data. It provides a reasoning engine and training system that enable vision-language models to generate step-by-step logical rationales and final answers for complex queries.
The framework utilizes a two-stage training pipeline that decouples the generation of logical justifications from final answer inference. It transforms visual data into descriptive text through image captioning and uses vision-transformer feature extraction to provide a linguistic foundation for the reasoning process.
The project includes a benchmark evaluator that measures model accuracy and logical consistency using semantic similarity and standardized prompt templates. It covers the full pipeline from visual feature extraction and model training to multimodal answer inference and reasoning performance evaluation.
Features
- Chain-of-Thought Reasoning Engines - Provides a logic processor that decouples the generation of step-by-step rationales from final model predictions.
- Feature Extraction - Converts raw image files into structured numerical representations to prepare visual data for model training.
- Image Captioning Models - Utilizes neural network models to generate textual descriptions of images as a foundation for reasoning.
- Vision-Language Trainers - Trains multimodal models to combine vision transformers and linguistic processing for logical answer generation.
- Vision-Language Training - Trains models to align visual and textual data to perform complex downstream reasoning tasks.
- Multimodal Rationales - Implements a system to produce step-by-step logical rationales based on combined text and image data to solve complex queries.
- Multimodal Reasoners - Provides a framework for training and evaluating large language models to perform multimodal chain-of-thought reasoning.
- Vision Transformer Encoders - Employs vision transformer encoders to convert raw visual data into structured embeddings for linguistic processing.
- Decoupled Rationale Training - Implements training processes that separate the generation of logical justifications from final answer inference.
- Reasoning Sequence Pipelines - Implements a two-stage pipeline that first develops logical rationales and then refines the final answer prediction.
- Multimodal Reasoning Evaluations - Measures model accuracy and logic using benchmarks that require combined image and text analysis.
- Complex Query Inference - Predicts answers to difficult questions by separating the generation of logical justifications from the final output.
- Multimodal Inference Pipelines - Processes multimodal data through trained reasoning models to predict answers for complex queries.
- Prompt Templates - Uses standardized prompt templates to ensure consistent reasoning patterns across multimodal benchmarks.
- Output Similarity Evaluators - Provides metrics to quantify the factual alignment and similarity between generated rationales and reference data.
- Chain Of Thought - Multimodal chain-of-thought reasoning for language models.
- Reasoning And Planning - Multimodal chain-of-thought reasoning for complex inputs.
Star history
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