CC-SHAP for VLMs 🖼️

Official code implementation for the paper "Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?" accepted at ICLR 2025! 🙌 https://openreview.net/forum?id=lCasyP21Bf

This is follow-up work building on the paper "On Measuring Faithfulness of Natural Language Explanations" https://aclanthology.org/2024.acl-long.329/ that developed CC-SHAP and applied it to LLMs 📃. Now, we extend to VLMs 🖼️+📃.

Cite

@inproceedings{parcalabescu2025do,
    title={Do Vision \& Language Decoders use Images and Text equally? How Self-consistent are their Explanations?},
    author={Letitia Parcalabescu and Anette Frank},
    booktitle={The Thirteenth International Conference on Learning Representations},
    year={2025},
    url={https://openreview.net/forum?id=lCasyP21Bf}
}

@inproceedings{parcalabescu-frank-2024-measuring,
    title = "On Measuring Faithfulness or Self-consistency of Natural Language Explanations",
    author = "Parcalabescu, Letitia  and
      Frank, Anette",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.329/",
    doi = "10.18653/v1/2024.acl-long.329",
    pages = "6048--6089",
}

Supported Models

1. mPLUG-Owl3 (second paper version) can be run from the main branch with the instructions in the next section.
2. BakLLaVA (first paper version) is experimental on main, because the hf transformers version had to be upgraded for supporting mPLUG-Owl3 -- upgrade which brought significant functionality changes. To run the model as it was run for the paper, check out the branch vicuna-mistral-bakllava-models and follow the instructions from the README.md file there.
3. LLaVA-NeXT-Vicuna (first paper version) same as above.
4. LLaVA-NeXT-Mistral (first paper version) same as above.

Installation and running

1. conda create -n <env-name> python=3.12.1
2. pip install -r requirements_pip-mplug-owl3.txt for installing the required packages with pip for running mPLUG-Owl3 experiments.
3. Download the data from their respective repositories and change the paths in config.py accordingly. Data repositories:

• VALSE 💃: https://github.com/Heidelberg-NLP/VALSE
• VQA: https://visualqa.org/download.html
• GQA: https://cs.stanford.edu/people/dorarad/gqa/download.html

3. To run mPLUG-Owl3, you need to make sure the _decode function in $HF_HOME/modules/transformers_modules/mPLUG/mPLUG-Owl3-7B-240728/eff25bcdc02ff1b513c25f376d761ec1ab6dfa1b/modeling_mplugowl3.py returns the output ids and not just the text, so move the line output = output[:,input_ids.shape[1]:] to the if statement such that the last lines of that function look like:

    if decode_text:
        output = output[:,input_ids.shape[1]:]
        return self._decode_text(output, tokenizer)
    return output

4. Run run-faithfulness.py with the following command python run-faithfulness.py foil_it mplug-owl3-7b 100 0 data/

Supported Tests

To activate tests individually, comment the respective elements of TESTS in config.py (cc_shap-posthoc and cc_shap-cot must be run together). All tests are implemented for the first three models in the other branch. The mPLUG-Owl3 model is only supported for the cc_shap-posthoc and cc_shap-cot tests.

Credits

The Shapley value implementation in the shap folder is a modified version of https://github.com/slundberg/shap .

Disclaimer

This is work in progress. Code and paper will be revised and improved for conference submissions.