@inproceedings{pan-etal-2026-context,
    title = "Context Attribution with Multi-Armed Bandit Optimization",
    author = "Pan, Deng  and
      Murugesan, Keerthiram  and
      Hua, Ting  and
      Moniz, Nuno  and
      Chawla, Nitesh V.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.565/",
    pages = "11651--11662",
    ISBN = "979-8-89176-395-1",
    abstract = "Understanding which parts of the retrieved context contribute to a large language model{'}s generated answer is essential for building interpretable and trustworthy retrieval-augmented generation. We propose a novel framework that formulates context attribution as a combinatorial multi-armed bandit problem. We utilize Linear Thompson Sampling to efficiently identify the most influential context segments while minimizing the number of model queries. Our reward function leverages token log-probabilities to measure how well a subset of segments supports the original response, making it applicable to both open-source and black-box API-based models. Unlike SHAP and other perturbation-based methods that sample subsets uniformly, our approach adaptively prioritizes informative subsets based on posterior estimates of segment relevance, reducing computational costs. Experiments on multiple QA benchmarks demonstrate that our method achieves up to 30{\%} reduction in model queries while matching or exceeding the attribution quality of existing approaches."
}