LLM-generated Black-box Explanations can be Adversarially Helpful

Arxiv:

The data are at

Scripts for reproducing the findings

• Section 4 Human evaluation: notebooks/analyze_human_and_proxy_results.ipynb (this notebook also collects the automatic evaluation results)
• Section 5 Automatic evaluation: scripts/proxy_evaluation.py
• Section 6 Strategies scripts/persuasion_strategy.py
• Section 7 A structural analysis notebooks/generate_graph_example.ipynb

Other scripts

• notebooks/check_proxy_completeness.ipynb is used to check the completeness of experiment results.
• scripts/llm.py contains a tool for prompting LLMs.

Citation

@article{ajwani2024generated,
    title={{LLM-generated Black-box Explanations can be Adversarially Helpful}},
    author={Ajwani, Rohan and Javaji, Shashidhar Reddy and Rudzicz, Frank and Zhu, Zining},
    journal={arXiv preprint arXiv:2405.06800},
    year={2024}
}