@inproceedings{choi-etal-2025-conflict,
    title = "Conflict-Aware Soft Prompting for Retrieval-Augmented Generation",
    author = "Choi, Eunseong  and
      Park, June  and
      Lee, Hyeri  and
      Lee, Jongwuk",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.1371/",
    doi = "10.18653/v1/2025.emnlp-main.1371",
    pages = "26969--26983",
    ISBN = "979-8-89176-332-6",
    abstract = "Retrieval-augmented generation (RAG) enhances the capabilities of large language models (LLMs) by incorporating external knowledge into their input prompts. However, when the retrieved context contradicts the LLM{'}s parametric knowledge, it often fails to resolve the conflict between incorrect external context and correct parametric knowledge, known as context-memory conflict. To tackle this problem, we introduce Conflict-Aware REtrieval-Augmented Generation (CARE), consisting of a context assessor and a base LLM. The context assessor encodes external context into compact memory embeddings. Through grounded/adversarial soft prompting, the context assessor is trained to discern unreliable context and capture a guidance signal that directs reasoning toward the more reliable knowledge source. Extensive experiments show that CARE effectively mitigates context-memory conflicts, leading to an average performance gain of 5.0{\%} on QA and fact-checking benchmarks, establishing a promising direction for trustworthy and adaptive RAG systems."
}