@inproceedings{ozaki-etal-2025-understanding,
    title = "Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain",
    author = "Ozaki, Shintaro  and
      Kato, Yuta  and
      Feng, Siyuan  and
      Tomita, Masayo  and
      Hayashi, Kazuki  and
      Hashimoto, Wataru  and
      Obara, Ryoma  and
      Oyamada, Masafumi  and
      Hayashi, Katsuhiko  and
      Kamigaito, Hidetaka  and
      Watanabe, Taro",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Tsujii, Junichi",
    booktitle = "ACL 2025",
    month = aug,
    year = "2025",
    address = "Viena, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-1.1/",
    pages = "1--17",
    ISBN = "979-8-89176-275-6",
    abstract = "Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries. This approach is widely applied in several fields by taking its advantage of injecting the most up-to-date information, and researchers are focusing on understanding and improving this aspect to unlock the full potential of RAG in such high-stakes applications.However, despite the potential of RAG to address these needs, the mechanisms behind the confidence levels of its outputs remain underexplored.Our study focuses on the impact of RAG, specifically examining whether RAG increases the confidence of LLM outputs in the medical domain.We conduct this analysis across various configurations and models.We evaluate confidence by treating the model{'}s predicted probability as its output and calculating several evaluation metrics which include calibration error method, entropy, best probability, and accuracy.Experimental results across multiple datasets confirmed that certain models possess the capability to judge for themselves whether an inserted document relates to the correct answer. These results suggest that evaluating models based on their output probabilities determine whether they function as generators in the RAG framework.Our approach allows to evaluate whether the models handle retrieved documents."
}