XinyiYS/detail_release

Official released code for paper title "DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning"

Generalizing Influence to Demontration Attribution in In-context Learning

Usage

• For running the MNIST experiment, install the conda environment jax_env.yml and run CUDA_VISIBLE_DEVICES=0 python mnist_data_attr.py
• For running the LLM experiments, install the conda environment torch_env.yml and run the programs described in script.sh
• To analyze the results, use analyzer.ipynb.

Acknowledgement

Parts of the code are referenced from the following repositories

• Transformers Learn In-context by Gradient Descent
• Label Words are Anchors
• Use Your INSTINCT: Instruction Optimization Using Neural Bandits Coupled with Transformers

Credit our work

If you find our work interesting, please star our repository. If you wish to cite our paper, you may use the following citation format

@misc{zhou2024detail,
      title={DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning}, 
      author={Zijian Zhou and Xiaoqiang Lin and Xinyi Xu and Alok Prakash and Daniela Rus and Bryan Kian Hsiang Low},
      year={2024},
      eprint={2405.14899},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}