🌟 MLLM-SHAP

Premium research platform for Shapley explanations in multimodal LLM systems.

🧭 Executive Overview

MLLM-SHAP combines production-grade package engineering with research-grade experiment tooling. Repository designed for teams that need explainability across text/audio model pipelines without losing reproducibility.

⚡ Quick Start

pip install mllm-shap

For full monorepo environment:

make install

🏗️ Repository Architecture

• mllm_shap/ - package source, tests, docs
• examples/ - end-to-end notebooks for explainability workflows
• experiments/ - dataset prep, config runner, analytics
• papers/ - publication assets and figure pipeline

🧰 Tooling Standards

• primary developer interface: make
• dependency and env management backend: uv
• code quality gates: pre-commit, black, isort, flake8
• documentation: Sphinx with autodoc
• packaging and docs pipeline: pyproject.toml + Sphinx

🔗 Primary Entry Points

• package guide: mllm_shap/README.md
• examples guide: examples/README.md
• experiments guide: experiments/README.md
• contribution rules: CONTRIBUTING.md

🔬 Research References

• Bridging Traditional Explainability Methods and Multimodal Multilingual Models
• SGPA: Spectrogram-Guided Phonetic Alignment for Feasible Shapley Value Explanations
• mllm-shap Zenodo release

📄 License

Apache License 2.0. See LICENSE.