@inproceedings{cifka-liutkus-2023-black,
    title = "Black-box language model explanation by context length probing",
    author = "C{\'i}fka, Ond{\v{r}}ej  and
      Liutkus, Antoine",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.acl-short.92/",
    doi = "10.18653/v1/2023.acl-short.92",
    pages = "1067--1079",
    abstract = "The increasingly widespread adoption of large language models has highlighted the need for improving their explainability. We present *context length probing*, a novel explanation technique for causal language models, based on tracking the predictions of a model as a function of the length of available context, and allowing to assign *differential importance scores* to different contexts. The technique is model-agnostic and does not rely on access to model internals beyond computing token-level probabilities. We apply context length probing to large pre-trained language models and offer some initial analyses and insights, including the potential for studying long-range dependencies. The [source code](\url{https://github.com/cifkao/context-probing/}) and an [interactive demo](\url{https://cifkao.github.io/context-probing/}) of the method are available."
}